ftopal/huggingface-models-processed · Datasets at Hugging Face

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text-generation	transformers	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) sqlcoder-7b-2 - bnb 4bits - Model creator: https://huggingface.co/defog/ - Original model: https://huggingface.co/defog/sqlcoder-7b-2/ Original model description: --- license: cc-by-sa-4.0 library_name: transformers pipeline_tag: text-generation --- # Update notice The model weights were updated at 7 AM UTC on Feb 7, 2024. The new model weights lead to a much more performant model – particularly for joins. If you downloaded the model before that, please redownload the weights for best performance. # Model Card for SQLCoder-7B-2 A capable large language model for natural language to SQL generation. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/603bbad3fd770a9997b57cb6/AYUE2y14vy2XkD9MZpScu.png) ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [Defog, Inc](https://defog.ai) - Model type: [Text to SQL] - License: [CC-by-SA-4.0] - Finetuned from model: [CodeLlama-7B] ### Model Sources [optional] - [HuggingFace:](https://huggingface.co/defog/sqlcoder-70b-alpha) - [GitHub:](https://github.com/defog-ai/sqlcoder) - [Demo:](https://defog.ai/sqlcoder-demo/) ## Uses This model is intended to be used by non-technical users to understand data inside their SQL databases. It is meant as an analytics tool, and not as a database admin tool. This model has not been trained to reject malicious requests from users with write access to databases, and should only be used by users with read-only access. ## How to Get Started with the Model Use the code [here](https://github.com/defog-ai/sqlcoder/blob/main/inference.py) to get started with the model. ## Prompt Please use the following prompt for optimal results. Please remember to use `do_sample=False` and `num_beams=4` for optimal results. ``` ### Task Generate a SQL query to answer [QUESTION]{user_question}[/QUESTION] ### Database Schema The query will run on a database with the following schema: {table_metadata_string_DDL_statements} ### Answer Given the database schema, here is the SQL query that [QUESTION]{user_question}[/QUESTION] [SQL] ``` ## Evaluation This model was evaluated on [SQL-Eval](https://github.com/defog-ai/sql-eval), a PostgreSQL based evaluation framework developed by Defog for testing and alignment of model capabilities. You can read more about the methodology behind SQLEval [here](https://defog.ai/blog/open-sourcing-sqleval/). ### Results We classified each generated question into one of 6 categories. The table displays the percentage of questions answered correctly by each model, broken down by category. \| \| date \| group_by \| order_by \| ratio \| join \| where \| \| -------------- \| ---- \| -------- \| -------- \| ----- \| ---- \| ----- \| \| sqlcoder-70b \| 96 \| 91.4 \| 97.1 \| 85.7 \| 97.1 \| 91.4 \| \| sqlcoder-7b-2 \| 96 \| 91.4 \| 94.3 \| 91.4 \| 94.3 \| 77.1 \| \| sqlcoder-34b \| 80 \| 94.3 \| 85.7 \| 77.1 \| 85.7 \| 80 \| \| gpt-4 \| 72 \| 94.3 \| 97.1 \| 80 \| 91.4 \| 80 \| \| gpt-4-turbo \| 76 \| 91.4 \| 91.4 \| 62.8 \| 88.6 \| 77.1 \| \| natural-sql-7b \| 56 \| 88.6 \| 85.7 \| 60 \| 88.6 \| 80 \| \| sqlcoder-7b \| 64 \| 82.9 \| 74.3 \| 54.3 \| 74.3 \| 74.3 \| \| gpt-3.5 \| 72 \| 77.1 \| 82.8 \| 34.3 \| 65.7 \| 71.4 \| \| claude-2 \| 52 \| 71.4 \| 74.3 \| 57.1 \| 65.7 \| 62.9 \| ## Model Card Contact Contact us on X at [@defogdata](https://twitter.com/defogdata), or on email at [founders@defog.ai](mailto:founders@defog.ai)	{}	RichardErkhov/defog_-_sqlcoder-7b-2-4bits	null	[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]	null	2024-05-03T19:09:04+00:00	[]	[]	TAGS #transformers #safetensors #llama #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us	Quantization made by Richard Erkhov. Github Discord Request more models sqlcoder-7b-2 - bnb 4bits * Model creator: URL * Original model: URL Original model description: --------------------------- license: cc-by-sa-4.0 library\_name: transformers pipeline\_tag: text-generation -------------------------------------------------------------------------------- Update notice ============= The model weights were updated at 7 AM UTC on Feb 7, 2024. The new model weights lead to a much more performant model – particularly for joins. If you downloaded the model before that, please redownload the weights for best performance. Model Card for SQLCoder-7B-2 ============================ A capable large language model for natural language to SQL generation. !image/png Model Details ------------- ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. * Developed by: Defog, Inc * Model type: [Text to SQL] * License: [CC-by-SA-4.0] * Finetuned from model: [CodeLlama-7B] ### Model Sources [optional] * HuggingFace: * GitHub: * Demo: Uses ---- This model is intended to be used by non-technical users to understand data inside their SQL databases. It is meant as an analytics tool, and not as a database admin tool. This model has not been trained to reject malicious requests from users with write access to databases, and should only be used by users with read-only access. How to Get Started with the Model --------------------------------- Use the code here to get started with the model. Prompt ------ Please use the following prompt for optimal results. Please remember to use 'do\_sample=False' and 'num\_beams=4' for optimal results. Evaluation ---------- This model was evaluated on SQL-Eval, a PostgreSQL based evaluation framework developed by Defog for testing and alignment of model capabilities. You can read more about the methodology behind SQLEval here. ### Results We classified each generated question into one of 6 categories. The table displays the percentage of questions answered correctly by each model, broken down by category. Model Card Contact ------------------ Contact us on X at @defogdata, or on email at founders@URL	[ "### Model Description\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n\n* Developed by: Defog, Inc\n* Model type: [Text to SQL]\n* License: [CC-by-SA-4.0]\n* Finetuned from model: [CodeLlama-7B]", "### Model Sources [optional]\n\n\n* HuggingFace:\n* GitHub:\n* Demo:\n\n\nUses\n----\n\n\nThis model is intended to be used by non-technical users to understand data inside their SQL databases. It is meant as an analytics tool, and not as a database admin tool.\n\n\nThis model has not been trained to reject malicious requests from users with write access to databases, and should only be used by users with read-only access.\n\n\nHow to Get Started with the Model\n---------------------------------\n\n\nUse the code here to get started with the model.\n\n\nPrompt\n------\n\n\nPlease use the following prompt for optimal results. Please remember to use 'do\\_sample=False' and 'num\\_beams=4' for optimal results.\n\n\nEvaluation\n----------\n\n\nThis model was evaluated on SQL-Eval, a PostgreSQL based evaluation framework developed by Defog for testing and alignment of model capabilities.\n\n\nYou can read more about the methodology behind SQLEval here.", "### Results\n\n\nWe classified each generated question into one of 6 categories. The table displays the percentage of questions answered correctly by each model, broken down by category.\n\n\n\nModel Card Contact\n------------------\n\n\nContact us on X at @defogdata, or on email at founders@URL" ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "### Model Description\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n\n* Developed by: Defog, Inc\n* Model type: [Text to SQL]\n* License: [CC-by-SA-4.0]\n* Finetuned from model: [CodeLlama-7B]", "### Model Sources [optional]\n\n\n* HuggingFace:\n* GitHub:\n* Demo:\n\n\nUses\n----\n\n\nThis model is intended to be used by non-technical users to understand data inside their SQL databases. It is meant as an analytics tool, and not as a database admin tool.\n\n\nThis model has not been trained to reject malicious requests from users with write access to databases, and should only be used by users with read-only access.\n\n\nHow to Get Started with the Model\n---------------------------------\n\n\nUse the code here to get started with the model.\n\n\nPrompt\n------\n\n\nPlease use the following prompt for optimal results. Please remember to use 'do\\_sample=False' and 'num\\_beams=4' for optimal results.\n\n\nEvaluation\n----------\n\n\nThis model was evaluated on SQL-Eval, a PostgreSQL based evaluation framework developed by Defog for testing and alignment of model capabilities.\n\n\nYou can read more about the methodology behind SQLEval here.", "### Results\n\n\nWe classified each generated question into one of 6 categories. The table displays the percentage of questions answered correctly by each model, broken down by category.\n\n\n\nModel Card Contact\n------------------\n\n\nContact us on X at @defogdata, or on email at founders@URL" ]	[ 38, 76, 241, 73 ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n### Model Description\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n\n* Developed by: Defog, Inc\n* Model type: [Text to SQL]\n* License: [CC-by-SA-4.0]\n* Finetuned from model: [CodeLlama-7B]### Model Sources [optional]\n\n\n* HuggingFace:\n* GitHub:\n* Demo:\n\n\nUses\n----\n\n\nThis model is intended to be used by non-technical users to understand data inside their SQL databases. It is meant as an analytics tool, and not as a database admin tool.\n\n\nThis model has not been trained to reject malicious requests from users with write access to databases, and should only be used by users with read-only access.\n\n\nHow to Get Started with the Model\n---------------------------------\n\n\nUse the code here to get started with the model.\n\n\nPrompt\n------\n\n\nPlease use the following prompt for optimal results. Please remember to use 'do\\_sample=False' and 'num\\_beams=4' for optimal results.\n\n\nEvaluation\n----------\n\n\nThis model was evaluated on SQL-Eval, a PostgreSQL based evaluation framework developed by Defog for testing and alignment of model capabilities.\n\n\nYou can read more about the methodology behind SQLEval here.### Results\n\n\nWe classified each generated question into one of 6 categories. The table displays the percentage of questions answered correctly by each model, broken down by category.\n\n\n\nModel Card Contact\n------------------\n\n\nContact us on X at @defogdata, or on email at founders@URL" ]
null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # GUE_tf_4-seqsight_4096_512_15M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_tf_4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_4) dataset. It achieves the following results on the evaluation set: - Loss: 0.3704 - F1 Score: 0.8588 - Accuracy: 0.859 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.5525 \| 1.34 \| 200 \| 0.5126 \| 0.7380 \| 0.738 \| \| 0.4762 \| 2.68 \| 400 \| 0.4937 \| 0.7496 \| 0.75 \| \| 0.4601 \| 4.03 \| 600 \| 0.4891 \| 0.7548 \| 0.756 \| \| 0.4466 \| 5.37 \| 800 \| 0.4828 \| 0.7580 \| 0.758 \| \| 0.4325 \| 6.71 \| 1000 \| 0.4821 \| 0.7670 \| 0.769 \| \| 0.4221 \| 8.05 \| 1200 \| 0.4624 \| 0.7769 \| 0.777 \| \| 0.416 \| 9.4 \| 1400 \| 0.4501 \| 0.7809 \| 0.781 \| \| 0.4062 \| 10.74 \| 1600 \| 0.4531 \| 0.7800 \| 0.78 \| \| 0.3994 \| 12.08 \| 1800 \| 0.4526 \| 0.7831 \| 0.784 \| \| 0.3951 \| 13.42 \| 2000 \| 0.4485 \| 0.7939 \| 0.794 \| \| 0.3826 \| 14.77 \| 2200 \| 0.4444 \| 0.7958 \| 0.796 \| \| 0.3825 \| 16.11 \| 2400 \| 0.4407 \| 0.7955 \| 0.796 \| \| 0.3734 \| 17.45 \| 2600 \| 0.4475 \| 0.7848 \| 0.785 \| \| 0.367 \| 18.79 \| 2800 \| 0.4480 \| 0.7940 \| 0.794 \| \| 0.3628 \| 20.13 \| 3000 \| 0.4385 \| 0.8019 \| 0.802 \| \| 0.3505 \| 21.48 \| 3200 \| 0.4360 \| 0.8079 \| 0.808 \| \| 0.3513 \| 22.82 \| 3400 \| 0.4419 \| 0.8037 \| 0.804 \| \| 0.345 \| 24.16 \| 3600 \| 0.4359 \| 0.8080 \| 0.808 \| \| 0.3405 \| 25.5 \| 3800 \| 0.4313 \| 0.8097 \| 0.81 \| \| 0.3327 \| 26.85 \| 4000 \| 0.4307 \| 0.8130 \| 0.813 \| \| 0.3347 \| 28.19 \| 4200 \| 0.4333 \| 0.7970 \| 0.797 \| \| 0.319 \| 29.53 \| 4400 \| 0.4489 \| 0.8188 \| 0.819 \| \| 0.3213 \| 30.87 \| 4600 \| 0.4355 \| 0.8050 \| 0.805 \| \| 0.3171 \| 32.21 \| 4800 \| 0.4279 \| 0.8090 \| 0.809 \| \| 0.3143 \| 33.56 \| 5000 \| 0.4330 \| 0.8120 \| 0.812 \| \| 0.3113 \| 34.9 \| 5200 \| 0.4400 \| 0.8070 \| 0.807 \| \| 0.3048 \| 36.24 \| 5400 \| 0.4414 \| 0.798 \| 0.798 \| \| 0.2986 \| 37.58 \| 5600 \| 0.4316 \| 0.8146 \| 0.815 \| \| 0.295 \| 38.93 \| 5800 \| 0.4465 \| 0.8040 \| 0.804 \| \| 0.295 \| 40.27 \| 6000 \| 0.4404 \| 0.8098 \| 0.81 \| \| 0.2883 \| 41.61 \| 6200 \| 0.4515 \| 0.8090 \| 0.809 \| \| 0.2897 \| 42.95 \| 6400 \| 0.4408 \| 0.8110 \| 0.811 \| \| 0.2857 \| 44.3 \| 6600 \| 0.4365 \| 0.8145 \| 0.815 \| \| 0.2787 \| 45.64 \| 6800 \| 0.4331 \| 0.8120 \| 0.812 \| \| 0.2862 \| 46.98 \| 7000 \| 0.4335 \| 0.8189 \| 0.819 \| \| 0.2767 \| 48.32 \| 7200 \| 0.4339 \| 0.8148 \| 0.815 \| \| 0.2712 \| 49.66 \| 7400 \| 0.4270 \| 0.8129 \| 0.813 \| \| 0.2712 \| 51.01 \| 7600 \| 0.4322 \| 0.8170 \| 0.817 \| \| 0.2708 \| 52.35 \| 7800 \| 0.4382 \| 0.8198 \| 0.82 \| \| 0.2644 \| 53.69 \| 8000 \| 0.4400 \| 0.8160 \| 0.816 \| \| 0.2678 \| 55.03 \| 8200 \| 0.4366 \| 0.8230 \| 0.823 \| \| 0.2635 \| 56.38 \| 8400 \| 0.4318 \| 0.8229 \| 0.823 \| \| 0.261 \| 57.72 \| 8600 \| 0.4403 \| 0.8178 \| 0.818 \| \| 0.262 \| 59.06 \| 8800 \| 0.4338 \| 0.8179 \| 0.818 \| \| 0.2617 \| 60.4 \| 9000 \| 0.4364 \| 0.8220 \| 0.822 \| \| 0.2545 \| 61.74 \| 9200 \| 0.4385 \| 0.8219 \| 0.822 \| \| 0.2568 \| 63.09 \| 9400 \| 0.4400 \| 0.8289 \| 0.829 \| \| 0.257 \| 64.43 \| 9600 \| 0.4372 \| 0.8239 \| 0.824 \| \| 0.2581 \| 65.77 \| 9800 \| 0.4372 \| 0.8249 \| 0.825 \| \| 0.2546 \| 67.11 \| 10000 \| 0.4370 \| 0.8259 \| 0.826 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_tf_4-seqsight_4096_512_15M-L32_f", "results": []}]}	mahdibaghbanzadeh/GUE_tf_4-seqsight_4096_512_15M-L32_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]	null	2024-05-03T19:09:48+00:00	[]	[]	TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us	GUE\_tf\_4-seqsight\_4096\_512\_15M-L32\_f ========================================== This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_tf\_4 dataset. It achieves the following results on the evaluation set: * Loss: 0.3704 * F1 Score: 0.8588 * Accuracy: 0.859 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]	[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]	[ 43, 100, 5, 52 ]	[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000### Training results### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]
null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # GUE_tf_3-seqsight_4096_512_15M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_tf_3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_3) dataset. It achieves the following results on the evaluation set: - Loss: 0.5670 - F1 Score: 0.6927 - Accuracy: 0.696 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.6616 \| 0.93 \| 200 \| 0.6000 \| 0.6809 \| 0.682 \| \| 0.618 \| 1.87 \| 400 \| 0.5894 \| 0.6799 \| 0.68 \| \| 0.6049 \| 2.8 \| 600 \| 0.5722 \| 0.7073 \| 0.711 \| \| 0.6017 \| 3.74 \| 800 \| 0.5692 \| 0.7085 \| 0.71 \| \| 0.5991 \| 4.67 \| 1000 \| 0.5638 \| 0.7147 \| 0.716 \| \| 0.5926 \| 5.61 \| 1200 \| 0.5632 \| 0.7197 \| 0.72 \| \| 0.5904 \| 6.54 \| 1400 \| 0.5591 \| 0.7143 \| 0.716 \| \| 0.5889 \| 7.48 \| 1600 \| 0.5586 \| 0.7229 \| 0.723 \| \| 0.5877 \| 8.41 \| 1800 \| 0.5571 \| 0.7163 \| 0.717 \| \| 0.59 \| 9.35 \| 2000 \| 0.5569 \| 0.7177 \| 0.719 \| \| 0.5839 \| 10.28 \| 2200 \| 0.5590 \| 0.7100 \| 0.71 \| \| 0.5842 \| 11.21 \| 2400 \| 0.5519 \| 0.7183 \| 0.72 \| \| 0.5841 \| 12.15 \| 2600 \| 0.5506 \| 0.7176 \| 0.721 \| \| 0.581 \| 13.08 \| 2800 \| 0.5494 \| 0.7161 \| 0.719 \| \| 0.5822 \| 14.02 \| 3000 \| 0.5530 \| 0.7166 \| 0.717 \| \| 0.5808 \| 14.95 \| 3200 \| 0.5503 \| 0.7212 \| 0.722 \| \| 0.5786 \| 15.89 \| 3400 \| 0.5493 \| 0.7234 \| 0.725 \| \| 0.5755 \| 16.82 \| 3600 \| 0.5515 \| 0.7176 \| 0.718 \| \| 0.5761 \| 17.76 \| 3800 \| 0.5495 \| 0.7271 \| 0.729 \| \| 0.5766 \| 18.69 \| 4000 \| 0.5525 \| 0.7197 \| 0.72 \| \| 0.5732 \| 19.63 \| 4200 \| 0.5478 \| 0.7169 \| 0.721 \| \| 0.5766 \| 20.56 \| 4400 \| 0.5462 \| 0.7184 \| 0.72 \| \| 0.5746 \| 21.5 \| 4600 \| 0.5500 \| 0.7120 \| 0.712 \| \| 0.5734 \| 22.43 \| 4800 \| 0.5467 \| 0.7263 \| 0.728 \| \| 0.5739 \| 23.36 \| 5000 \| 0.5478 \| 0.7246 \| 0.725 \| \| 0.5734 \| 24.3 \| 5200 \| 0.5494 \| 0.7121 \| 0.712 \| \| 0.5696 \| 25.23 \| 5400 \| 0.5453 \| 0.7188 \| 0.722 \| \| 0.5745 \| 26.17 \| 5600 \| 0.5448 \| 0.7234 \| 0.725 \| \| 0.568 \| 27.1 \| 5800 \| 0.5439 \| 0.7209 \| 0.724 \| \| 0.5682 \| 28.04 \| 6000 \| 0.5437 \| 0.7299 \| 0.731 \| \| 0.569 \| 28.97 \| 6200 \| 0.5486 \| 0.7161 \| 0.716 \| \| 0.5717 \| 29.91 \| 6400 \| 0.5448 \| 0.7316 \| 0.733 \| \| 0.5681 \| 30.84 \| 6600 \| 0.5447 \| 0.7337 \| 0.735 \| \| 0.5686 \| 31.78 \| 6800 \| 0.5464 \| 0.7217 \| 0.722 \| \| 0.5681 \| 32.71 \| 7000 \| 0.5444 \| 0.7319 \| 0.733 \| \| 0.5714 \| 33.64 \| 7200 \| 0.5447 \| 0.7315 \| 0.733 \| \| 0.5642 \| 34.58 \| 7400 \| 0.5480 \| 0.7131 \| 0.713 \| \| 0.5704 \| 35.51 \| 7600 \| 0.5458 \| 0.7226 \| 0.723 \| \| 0.5689 \| 36.45 \| 7800 \| 0.5453 \| 0.7246 \| 0.725 \| \| 0.5676 \| 37.38 \| 8000 \| 0.5453 \| 0.7236 \| 0.724 \| \| 0.5647 \| 38.32 \| 8200 \| 0.5449 \| 0.7317 \| 0.733 \| \| 0.5652 \| 39.25 \| 8400 \| 0.5451 \| 0.7284 \| 0.729 \| \| 0.5662 \| 40.19 \| 8600 \| 0.5453 \| 0.7284 \| 0.729 \| \| 0.5649 \| 41.12 \| 8800 \| 0.5455 \| 0.7275 \| 0.728 \| \| 0.5682 \| 42.06 \| 9000 \| 0.5454 \| 0.7285 \| 0.729 \| \| 0.5665 \| 42.99 \| 9200 \| 0.5461 \| 0.7217 \| 0.722 \| \| 0.565 \| 43.93 \| 9400 \| 0.5464 \| 0.7199 \| 0.72 \| \| 0.5637 \| 44.86 \| 9600 \| 0.5452 \| 0.7266 \| 0.727 \| \| 0.5659 \| 45.79 \| 9800 \| 0.5451 \| 0.7285 \| 0.729 \| \| 0.562 \| 46.73 \| 10000 \| 0.5452 \| 0.7256 \| 0.726 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_tf_3-seqsight_4096_512_15M-L1_f", "results": []}]}	mahdibaghbanzadeh/GUE_tf_3-seqsight_4096_512_15M-L1_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]	null	2024-05-03T19:09:48+00:00	[]	[]	TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us	GUE\_tf\_3-seqsight\_4096\_512\_15M-L1\_f ========================================= This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_tf\_3 dataset. It achieves the following results on the evaluation set: * Loss: 0.5670 * F1 Score: 0.6927 * Accuracy: 0.696 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]	[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]	[ 43, 100, 5, 52 ]	[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000### Training results### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]
null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # GUE_tf_3-seqsight_4096_512_15M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_tf_3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_3) dataset. It achieves the following results on the evaluation set: - Loss: 0.5516 - F1 Score: 0.7033 - Accuracy: 0.706 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.6433 \| 0.93 \| 200 \| 0.5805 \| 0.7011 \| 0.701 \| \| 0.6017 \| 1.87 \| 400 \| 0.5751 \| 0.7004 \| 0.701 \| \| 0.594 \| 2.8 \| 600 \| 0.5608 \| 0.7085 \| 0.711 \| \| 0.5899 \| 3.74 \| 800 \| 0.5582 \| 0.7090 \| 0.709 \| \| 0.5889 \| 4.67 \| 1000 \| 0.5522 \| 0.7134 \| 0.714 \| \| 0.5826 \| 5.61 \| 1200 \| 0.5491 \| 0.7141 \| 0.715 \| \| 0.5801 \| 6.54 \| 1400 \| 0.5494 \| 0.7171 \| 0.718 \| \| 0.5778 \| 7.48 \| 1600 \| 0.5482 \| 0.7223 \| 0.723 \| \| 0.5758 \| 8.41 \| 1800 \| 0.5475 \| 0.7218 \| 0.722 \| \| 0.5787 \| 9.35 \| 2000 \| 0.5472 \| 0.7054 \| 0.709 \| \| 0.5717 \| 10.28 \| 2200 \| 0.5482 \| 0.7199 \| 0.72 \| \| 0.5721 \| 11.21 \| 2400 \| 0.5441 \| 0.7227 \| 0.724 \| \| 0.5709 \| 12.15 \| 2600 \| 0.5453 \| 0.7008 \| 0.707 \| \| 0.5673 \| 13.08 \| 2800 \| 0.5479 \| 0.6937 \| 0.701 \| \| 0.5676 \| 14.02 \| 3000 \| 0.5444 \| 0.7196 \| 0.721 \| \| 0.5661 \| 14.95 \| 3200 \| 0.5459 \| 0.7086 \| 0.712 \| \| 0.5641 \| 15.89 \| 3400 \| 0.5448 \| 0.7142 \| 0.716 \| \| 0.5601 \| 16.82 \| 3600 \| 0.5457 \| 0.7172 \| 0.719 \| \| 0.5597 \| 17.76 \| 3800 \| 0.5455 \| 0.7127 \| 0.716 \| \| 0.5602 \| 18.69 \| 4000 \| 0.5471 \| 0.7187 \| 0.719 \| \| 0.558 \| 19.63 \| 4200 \| 0.5495 \| 0.7043 \| 0.709 \| \| 0.559 \| 20.56 \| 4400 \| 0.5477 \| 0.7125 \| 0.716 \| \| 0.5577 \| 21.5 \| 4600 \| 0.5518 \| 0.7161 \| 0.716 \| \| 0.5555 \| 22.43 \| 4800 \| 0.5469 \| 0.7103 \| 0.714 \| \| 0.5556 \| 23.36 \| 5000 \| 0.5495 \| 0.7171 \| 0.717 \| \| 0.5544 \| 24.3 \| 5200 \| 0.5554 \| 0.6955 \| 0.696 \| \| 0.5502 \| 25.23 \| 5400 \| 0.5482 \| 0.7157 \| 0.719 \| \| 0.5575 \| 26.17 \| 5600 \| 0.5434 \| 0.7264 \| 0.728 \| \| 0.5477 \| 27.1 \| 5800 \| 0.5433 \| 0.7174 \| 0.719 \| \| 0.5481 \| 28.04 \| 6000 \| 0.5441 \| 0.7282 \| 0.73 \| \| 0.5482 \| 28.97 \| 6200 \| 0.5480 \| 0.7231 \| 0.723 \| \| 0.5491 \| 29.91 \| 6400 \| 0.5455 \| 0.7245 \| 0.727 \| \| 0.5473 \| 30.84 \| 6600 \| 0.5441 \| 0.7217 \| 0.723 \| \| 0.5492 \| 31.78 \| 6800 \| 0.5472 \| 0.7217 \| 0.722 \| \| 0.5466 \| 32.71 \| 7000 \| 0.5442 \| 0.7272 \| 0.728 \| \| 0.5503 \| 33.64 \| 7200 \| 0.5444 \| 0.7283 \| 0.73 \| \| 0.542 \| 34.58 \| 7400 \| 0.5502 \| 0.7191 \| 0.719 \| \| 0.5477 \| 35.51 \| 7600 \| 0.5458 \| 0.7290 \| 0.729 \| \| 0.5467 \| 36.45 \| 7800 \| 0.5461 \| 0.7257 \| 0.726 \| \| 0.5466 \| 37.38 \| 8000 \| 0.5456 \| 0.7278 \| 0.728 \| \| 0.5417 \| 38.32 \| 8200 \| 0.5471 \| 0.7259 \| 0.727 \| \| 0.5427 \| 39.25 \| 8400 \| 0.5465 \| 0.7237 \| 0.724 \| \| 0.5423 \| 40.19 \| 8600 \| 0.5461 \| 0.7255 \| 0.726 \| \| 0.5414 \| 41.12 \| 8800 \| 0.5461 \| 0.7285 \| 0.729 \| \| 0.5451 \| 42.06 \| 9000 \| 0.5452 \| 0.7277 \| 0.728 \| \| 0.5428 \| 42.99 \| 9200 \| 0.5468 \| 0.7259 \| 0.726 \| \| 0.541 \| 43.93 \| 9400 \| 0.5469 \| 0.7259 \| 0.726 \| \| 0.538 \| 44.86 \| 9600 \| 0.5463 \| 0.7257 \| 0.726 \| \| 0.5423 \| 45.79 \| 9800 \| 0.5461 \| 0.7293 \| 0.73 \| \| 0.5373 \| 46.73 \| 10000 \| 0.5468 \| 0.7248 \| 0.725 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_tf_3-seqsight_4096_512_15M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_tf_3-seqsight_4096_512_15M-L8_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]	null	2024-05-03T19:10:15+00:00	[]	[]	TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us	GUE\_tf\_3-seqsight\_4096\_512\_15M-L8\_f ========================================= This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_tf\_3 dataset. It achieves the following results on the evaluation set: * Loss: 0.5516 * F1 Score: 0.7033 * Accuracy: 0.706 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]	[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]	[ 43, 100, 5, 52 ]	[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000### Training results### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]
null	null	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # GUE_tf_2-seqsight_4096_512_15M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_tf_2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_2) dataset. It achieves the following results on the evaluation set: - Loss: 0.4762 - F1 Score: 0.7710 - Accuracy: 0.771 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.6238 \| 1.34 \| 200 \| 0.5599 \| 0.7084 \| 0.71 \| \| 0.5565 \| 2.68 \| 400 \| 0.5340 \| 0.7329 \| 0.733 \| \| 0.538 \| 4.03 \| 600 \| 0.5271 \| 0.7310 \| 0.731 \| \| 0.5338 \| 5.37 \| 800 \| 0.5242 \| 0.7350 \| 0.735 \| \| 0.5298 \| 6.71 \| 1000 \| 0.5203 \| 0.7365 \| 0.737 \| \| 0.5247 \| 8.05 \| 1200 \| 0.5171 \| 0.7490 \| 0.749 \| \| 0.5214 \| 9.4 \| 1400 \| 0.5140 \| 0.7415 \| 0.742 \| \| 0.5209 \| 10.74 \| 1600 \| 0.5141 \| 0.7418 \| 0.742 \| \| 0.5181 \| 12.08 \| 1800 \| 0.5195 \| 0.7438 \| 0.744 \| \| 0.5183 \| 13.42 \| 2000 \| 0.5135 \| 0.7440 \| 0.744 \| \| 0.5182 \| 14.77 \| 2200 \| 0.5122 \| 0.7470 \| 0.747 \| \| 0.5123 \| 16.11 \| 2400 \| 0.5162 \| 0.7407 \| 0.741 \| \| 0.5154 \| 17.45 \| 2600 \| 0.5111 \| 0.7399 \| 0.74 \| \| 0.5098 \| 18.79 \| 2800 \| 0.5099 \| 0.7400 \| 0.74 \| \| 0.5091 \| 20.13 \| 3000 \| 0.5103 \| 0.7400 \| 0.74 \| \| 0.5095 \| 21.48 \| 3200 \| 0.5116 \| 0.7359 \| 0.736 \| \| 0.5106 \| 22.82 \| 3400 \| 0.5074 \| 0.7399 \| 0.74 \| \| 0.5052 \| 24.16 \| 3600 \| 0.5060 \| 0.7358 \| 0.736 \| \| 0.5024 \| 25.5 \| 3800 \| 0.5064 \| 0.7342 \| 0.735 \| \| 0.505 \| 26.85 \| 4000 \| 0.5060 \| 0.7375 \| 0.738 \| \| 0.5014 \| 28.19 \| 4200 \| 0.5058 \| 0.7340 \| 0.734 \| \| 0.5024 \| 29.53 \| 4400 \| 0.5097 \| 0.7410 \| 0.741 \| \| 0.5034 \| 30.87 \| 4600 \| 0.5076 \| 0.7380 \| 0.738 \| \| 0.5015 \| 32.21 \| 4800 \| 0.5058 \| 0.7390 \| 0.739 \| \| 0.5012 \| 33.56 \| 5000 \| 0.5107 \| 0.7417 \| 0.742 \| \| 0.5032 \| 34.9 \| 5200 \| 0.5063 \| 0.7389 \| 0.739 \| \| 0.4975 \| 36.24 \| 5400 \| 0.5017 \| 0.7367 \| 0.737 \| \| 0.4993 \| 37.58 \| 5600 \| 0.5034 \| 0.7420 \| 0.742 \| \| 0.4966 \| 38.93 \| 5800 \| 0.5047 \| 0.7370 \| 0.737 \| \| 0.497 \| 40.27 \| 6000 \| 0.5033 \| 0.7360 \| 0.736 \| \| 0.4973 \| 41.61 \| 6200 \| 0.5028 \| 0.7320 \| 0.732 \| \| 0.4951 \| 42.95 \| 6400 \| 0.5043 \| 0.7340 \| 0.734 \| \| 0.4949 \| 44.3 \| 6600 \| 0.5056 \| 0.7370 \| 0.737 \| \| 0.4977 \| 45.64 \| 6800 \| 0.5057 \| 0.7420 \| 0.742 \| \| 0.4943 \| 46.98 \| 7000 \| 0.5042 \| 0.7400 \| 0.74 \| \| 0.4949 \| 48.32 \| 7200 \| 0.5059 \| 0.7380 \| 0.738 \| \| 0.4923 \| 49.66 \| 7400 \| 0.5017 \| 0.7390 \| 0.739 \| \| 0.4941 \| 51.01 \| 7600 \| 0.5031 \| 0.7400 \| 0.74 \| \| 0.4942 \| 52.35 \| 7800 \| 0.5022 \| 0.7390 \| 0.739 \| \| 0.4957 \| 53.69 \| 8000 \| 0.5019 \| 0.7299 \| 0.73 \| \| 0.492 \| 55.03 \| 8200 \| 0.5023 \| 0.7410 \| 0.741 \| \| 0.4959 \| 56.38 \| 8400 \| 0.5038 \| 0.7400 \| 0.74 \| \| 0.494 \| 57.72 \| 8600 \| 0.5026 \| 0.7370 \| 0.737 \| \| 0.4905 \| 59.06 \| 8800 \| 0.5026 \| 0.7340 \| 0.734 \| \| 0.4909 \| 60.4 \| 9000 \| 0.5039 \| 0.7390 \| 0.739 \| \| 0.4921 \| 61.74 \| 9200 \| 0.5022 \| 0.7360 \| 0.736 \| \| 0.4956 \| 63.09 \| 9400 \| 0.5020 \| 0.7360 \| 0.736 \| \| 0.4896 \| 64.43 \| 9600 \| 0.5025 \| 0.7380 \| 0.738 \| \| 0.4913 \| 65.77 \| 9800 \| 0.5032 \| 0.7370 \| 0.737 \| \| 0.4887 \| 67.11 \| 10000 \| 0.5025 \| 0.7370 \| 0.737 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_tf_2-seqsight_4096_512_15M-L1_f", "results": []}]}	mahdibaghbanzadeh/GUE_tf_2-seqsight_4096_512_15M-L1_f	null	[ "region:us" ]	null	2024-05-03T19:11:10+00:00	[]	[]	TAGS #region-us	GUE\_tf\_2-seqsight\_4096\_512\_15M-L1\_f ========================================= This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_tf\_2 dataset. It achieves the following results on the evaluation set: * Loss: 0.4762 * F1 Score: 0.7710 * Accuracy: 0.771 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]	[ "TAGS\n#region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]	[ 5, 100, 5, 52 ]	[ "TAGS\n#region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000### Training results### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]
null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	ferrazzipietro/LS_Llama-2-7b-hf_adapters_en.layer1_NoQuant_32_32_0.05_2_5e-05	null	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-03T19:11:20+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. 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(2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. 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More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 26, 6, 4, 75, 23, 3, 5, 8, 9, 8, 34, 20, 4, 5, 5, 11, 13, 12, 3, 10, 6, 5, 6, 4, 5, 7, 49, 7, 7, 5, 5, 15, 7, 7, 8, 5 ]	[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	Armandodelca/Prototipo_7_EMI	null	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-03T19:12:51+00:00	[ "1910.09700" ]	[]	TAGS #transformers #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 22, 6, 4, 75, 23, 3, 5, 8, 9, 8, 34, 20, 4, 5, 5, 11, 13, 12, 3, 10, 6, 5, 6, 4, 5, 7, 49, 7, 7, 5, 5, 15, 7, 7, 8, 5 ]	[ "TAGS\n#transformers #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
null	null	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) sqlcoder-7b-2 - bnb 8bits - Model creator: https://huggingface.co/defog/ - Original model: https://huggingface.co/defog/sqlcoder-7b-2/ Original model description: --- license: cc-by-sa-4.0 library_name: transformers pipeline_tag: text-generation --- # Update notice The model weights were updated at 7 AM UTC on Feb 7, 2024. The new model weights lead to a much more performant model – particularly for joins. If you downloaded the model before that, please redownload the weights for best performance. # Model Card for SQLCoder-7B-2 A capable large language model for natural language to SQL generation. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/603bbad3fd770a9997b57cb6/AYUE2y14vy2XkD9MZpScu.png) ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [Defog, Inc](https://defog.ai) - Model type: [Text to SQL] - License: [CC-by-SA-4.0] - Finetuned from model: [CodeLlama-7B] ### Model Sources [optional] - [HuggingFace:](https://huggingface.co/defog/sqlcoder-70b-alpha) - [GitHub:](https://github.com/defog-ai/sqlcoder) - [Demo:](https://defog.ai/sqlcoder-demo/) ## Uses This model is intended to be used by non-technical users to understand data inside their SQL databases. It is meant as an analytics tool, and not as a database admin tool. This model has not been trained to reject malicious requests from users with write access to databases, and should only be used by users with read-only access. ## How to Get Started with the Model Use the code [here](https://github.com/defog-ai/sqlcoder/blob/main/inference.py) to get started with the model. ## Prompt Please use the following prompt for optimal results. Please remember to use `do_sample=False` and `num_beams=4` for optimal results. ``` ### Task Generate a SQL query to answer [QUESTION]{user_question}[/QUESTION] ### Database Schema The query will run on a database with the following schema: {table_metadata_string_DDL_statements} ### Answer Given the database schema, here is the SQL query that [QUESTION]{user_question}[/QUESTION] [SQL] ``` ## Evaluation This model was evaluated on [SQL-Eval](https://github.com/defog-ai/sql-eval), a PostgreSQL based evaluation framework developed by Defog for testing and alignment of model capabilities. You can read more about the methodology behind SQLEval [here](https://defog.ai/blog/open-sourcing-sqleval/). ### Results We classified each generated question into one of 6 categories. The table displays the percentage of questions answered correctly by each model, broken down by category. \| \| date \| group_by \| order_by \| ratio \| join \| where \| \| -------------- \| ---- \| -------- \| -------- \| ----- \| ---- \| ----- \| \| sqlcoder-70b \| 96 \| 91.4 \| 97.1 \| 85.7 \| 97.1 \| 91.4 \| \| sqlcoder-7b-2 \| 96 \| 91.4 \| 94.3 \| 91.4 \| 94.3 \| 77.1 \| \| sqlcoder-34b \| 80 \| 94.3 \| 85.7 \| 77.1 \| 85.7 \| 80 \| \| gpt-4 \| 72 \| 94.3 \| 97.1 \| 80 \| 91.4 \| 80 \| \| gpt-4-turbo \| 76 \| 91.4 \| 91.4 \| 62.8 \| 88.6 \| 77.1 \| \| natural-sql-7b \| 56 \| 88.6 \| 85.7 \| 60 \| 88.6 \| 80 \| \| sqlcoder-7b \| 64 \| 82.9 \| 74.3 \| 54.3 \| 74.3 \| 74.3 \| \| gpt-3.5 \| 72 \| 77.1 \| 82.8 \| 34.3 \| 65.7 \| 71.4 \| \| claude-2 \| 52 \| 71.4 \| 74.3 \| 57.1 \| 65.7 \| 62.9 \| ## Model Card Contact Contact us on X at [@defogdata](https://twitter.com/defogdata), or on email at [founders@defog.ai](mailto:founders@defog.ai)	{}	RichardErkhov/defog_-_sqlcoder-7b-2-8bits	null	[ "safetensors", "region:us" ]	null	2024-05-03T19:13:29+00:00	[]	[]	TAGS #safetensors #region-us	Quantization made by Richard Erkhov. Github Discord Request more models sqlcoder-7b-2 - bnb 8bits * Model creator: URL * Original model: URL Original model description: --------------------------- license: cc-by-sa-4.0 library\_name: transformers pipeline\_tag: text-generation -------------------------------------------------------------------------------- Update notice ============= The model weights were updated at 7 AM UTC on Feb 7, 2024. The new model weights lead to a much more performant model – particularly for joins. If you downloaded the model before that, please redownload the weights for best performance. Model Card for SQLCoder-7B-2 ============================ A capable large language model for natural language to SQL generation. !image/png Model Details ------------- ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. * Developed by: Defog, Inc * Model type: [Text to SQL] * License: [CC-by-SA-4.0] * Finetuned from model: [CodeLlama-7B] ### Model Sources [optional] * HuggingFace: * GitHub: * Demo: Uses ---- This model is intended to be used by non-technical users to understand data inside their SQL databases. It is meant as an analytics tool, and not as a database admin tool. This model has not been trained to reject malicious requests from users with write access to databases, and should only be used by users with read-only access. How to Get Started with the Model --------------------------------- Use the code here to get started with the model. Prompt ------ Please use the following prompt for optimal results. Please remember to use 'do\_sample=False' and 'num\_beams=4' for optimal results. Evaluation ---------- This model was evaluated on SQL-Eval, a PostgreSQL based evaluation framework developed by Defog for testing and alignment of model capabilities. You can read more about the methodology behind SQLEval here. ### Results We classified each generated question into one of 6 categories. The table displays the percentage of questions answered correctly by each model, broken down by category. Model Card Contact ------------------ Contact us on X at @defogdata, or on email at founders@URL	[ "### Model Description\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n\n* Developed by: Defog, Inc\n* Model type: [Text to SQL]\n* License: [CC-by-SA-4.0]\n* Finetuned from model: [CodeLlama-7B]", "### Model Sources [optional]\n\n\n* HuggingFace:\n* GitHub:\n* Demo:\n\n\nUses\n----\n\n\nThis model is intended to be used by non-technical users to understand data inside their SQL databases. It is meant as an analytics tool, and not as a database admin tool.\n\n\nThis model has not been trained to reject malicious requests from users with write access to databases, and should only be used by users with read-only access.\n\n\nHow to Get Started with the Model\n---------------------------------\n\n\nUse the code here to get started with the model.\n\n\nPrompt\n------\n\n\nPlease use the following prompt for optimal results. Please remember to use 'do\\_sample=False' and 'num\\_beams=4' for optimal results.\n\n\nEvaluation\n----------\n\n\nThis model was evaluated on SQL-Eval, a PostgreSQL based evaluation framework developed by Defog for testing and alignment of model capabilities.\n\n\nYou can read more about the methodology behind SQLEval here.", "### Results\n\n\nWe classified each generated question into one of 6 categories. The table displays the percentage of questions answered correctly by each model, broken down by category.\n\n\n\nModel Card Contact\n------------------\n\n\nContact us on X at @defogdata, or on email at founders@URL" ]	[ "TAGS\n#safetensors #region-us \n", "### Model Description\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n\n* Developed by: Defog, Inc\n* Model type: [Text to SQL]\n* License: [CC-by-SA-4.0]\n* Finetuned from model: [CodeLlama-7B]", "### Model Sources [optional]\n\n\n* HuggingFace:\n* GitHub:\n* Demo:\n\n\nUses\n----\n\n\nThis model is intended to be used by non-technical users to understand data inside their SQL databases. It is meant as an analytics tool, and not as a database admin tool.\n\n\nThis model has not been trained to reject malicious requests from users with write access to databases, and should only be used by users with read-only access.\n\n\nHow to Get Started with the Model\n---------------------------------\n\n\nUse the code here to get started with the model.\n\n\nPrompt\n------\n\n\nPlease use the following prompt for optimal results. Please remember to use 'do\\_sample=False' and 'num\\_beams=4' for optimal results.\n\n\nEvaluation\n----------\n\n\nThis model was evaluated on SQL-Eval, a PostgreSQL based evaluation framework developed by Defog for testing and alignment of model capabilities.\n\n\nYou can read more about the methodology behind SQLEval here.", "### Results\n\n\nWe classified each generated question into one of 6 categories. The table displays the percentage of questions answered correctly by each model, broken down by category.\n\n\n\nModel Card Contact\n------------------\n\n\nContact us on X at @defogdata, or on email at founders@URL" ]	[ 9, 76, 241, 73 ]	[ "TAGS\n#safetensors #region-us \n### Model Description\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n\n* Developed by: Defog, Inc\n* Model type: [Text to SQL]\n* License: [CC-by-SA-4.0]\n* Finetuned from model: [CodeLlama-7B]### Model Sources [optional]\n\n\n* HuggingFace:\n* GitHub:\n* Demo:\n\n\nUses\n----\n\n\nThis model is intended to be used by non-technical users to understand data inside their SQL databases. It is meant as an analytics tool, and not as a database admin tool.\n\n\nThis model has not been trained to reject malicious requests from users with write access to databases, and should only be used by users with read-only access.\n\n\nHow to Get Started with the Model\n---------------------------------\n\n\nUse the code here to get started with the model.\n\n\nPrompt\n------\n\n\nPlease use the following prompt for optimal results. Please remember to use 'do\\_sample=False' and 'num\\_beams=4' for optimal results.\n\n\nEvaluation\n----------\n\n\nThis model was evaluated on SQL-Eval, a PostgreSQL based evaluation framework developed by Defog for testing and alignment of model capabilities.\n\n\nYou can read more about the methodology behind SQLEval here.### Results\n\n\nWe classified each generated question into one of 6 categories. The table displays the percentage of questions answered correctly by each model, broken down by category.\n\n\n\nModel Card Contact\n------------------\n\n\nContact us on X at @defogdata, or on email at founders@URL" ]
null	null	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # GUE_tf_2-seqsight_4096_512_15M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_tf_2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_2) dataset. It achieves the following results on the evaluation set: - Loss: 0.4762 - F1 Score: 0.7889 - Accuracy: 0.789 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.5955 \| 1.34 \| 200 \| 0.5400 \| 0.7222 \| 0.723 \| \| 0.5387 \| 2.68 \| 400 \| 0.5279 \| 0.7378 \| 0.738 \| \| 0.5259 \| 4.03 \| 600 \| 0.5212 \| 0.7440 \| 0.744 \| \| 0.5216 \| 5.37 \| 800 \| 0.5194 \| 0.7418 \| 0.742 \| \| 0.5186 \| 6.71 \| 1000 \| 0.5178 \| 0.7380 \| 0.738 \| \| 0.5121 \| 8.05 \| 1200 \| 0.5113 \| 0.7370 \| 0.737 \| \| 0.5072 \| 9.4 \| 1400 \| 0.5088 \| 0.7378 \| 0.738 \| \| 0.5049 \| 10.74 \| 1600 \| 0.5100 \| 0.7390 \| 0.739 \| \| 0.5029 \| 12.08 \| 1800 \| 0.5164 \| 0.7475 \| 0.748 \| \| 0.4997 \| 13.42 \| 2000 \| 0.5137 \| 0.7435 \| 0.744 \| \| 0.5004 \| 14.77 \| 2200 \| 0.5058 \| 0.7422 \| 0.743 \| \| 0.4932 \| 16.11 \| 2400 \| 0.5088 \| 0.7445 \| 0.745 \| \| 0.4954 \| 17.45 \| 2600 \| 0.5046 \| 0.7419 \| 0.742 \| \| 0.489 \| 18.79 \| 2800 \| 0.4987 \| 0.7417 \| 0.742 \| \| 0.4875 \| 20.13 \| 3000 \| 0.5027 \| 0.7400 \| 0.74 \| \| 0.486 \| 21.48 \| 3200 \| 0.5136 \| 0.7389 \| 0.74 \| \| 0.4861 \| 22.82 \| 3400 \| 0.5056 \| 0.7339 \| 0.734 \| \| 0.4817 \| 24.16 \| 3600 \| 0.4967 \| 0.7400 \| 0.74 \| \| 0.4779 \| 25.5 \| 3800 \| 0.4973 \| 0.7370 \| 0.737 \| \| 0.4792 \| 26.85 \| 4000 \| 0.5002 \| 0.7398 \| 0.74 \| \| 0.4759 \| 28.19 \| 4200 \| 0.5024 \| 0.7369 \| 0.737 \| \| 0.4746 \| 29.53 \| 4400 \| 0.5073 \| 0.7470 \| 0.747 \| \| 0.4749 \| 30.87 \| 4600 \| 0.5034 \| 0.7409 \| 0.741 \| \| 0.4733 \| 32.21 \| 4800 \| 0.4998 \| 0.7419 \| 0.742 \| \| 0.4726 \| 33.56 \| 5000 \| 0.5061 \| 0.7393 \| 0.74 \| \| 0.4737 \| 34.9 \| 5200 \| 0.5063 \| 0.7414 \| 0.742 \| \| 0.4669 \| 36.24 \| 5400 \| 0.4962 \| 0.7449 \| 0.745 \| \| 0.469 \| 37.58 \| 5600 \| 0.5000 \| 0.7450 \| 0.745 \| \| 0.4658 \| 38.93 \| 5800 \| 0.5001 \| 0.7380 \| 0.738 \| \| 0.4631 \| 40.27 \| 6000 \| 0.5003 \| 0.7379 \| 0.738 \| \| 0.464 \| 41.61 \| 6200 \| 0.4970 \| 0.7400 \| 0.74 \| \| 0.4623 \| 42.95 \| 6400 \| 0.5046 \| 0.7459 \| 0.746 \| \| 0.46 \| 44.3 \| 6600 \| 0.5083 \| 0.7489 \| 0.749 \| \| 0.4634 \| 45.64 \| 6800 \| 0.5060 \| 0.7437 \| 0.744 \| \| 0.4588 \| 46.98 \| 7000 \| 0.5045 \| 0.7439 \| 0.744 \| \| 0.4597 \| 48.32 \| 7200 \| 0.5028 \| 0.746 \| 0.746 \| \| 0.4557 \| 49.66 \| 7400 \| 0.5030 \| 0.7510 \| 0.751 \| \| 0.4585 \| 51.01 \| 7600 \| 0.5068 \| 0.7386 \| 0.739 \| \| 0.4579 \| 52.35 \| 7800 \| 0.5012 \| 0.7440 \| 0.744 \| \| 0.4594 \| 53.69 \| 8000 \| 0.5003 \| 0.7460 \| 0.746 \| \| 0.4561 \| 55.03 \| 8200 \| 0.5002 \| 0.7450 \| 0.745 \| \| 0.4584 \| 56.38 \| 8400 \| 0.5024 \| 0.7428 \| 0.743 \| \| 0.4565 \| 57.72 \| 8600 \| 0.5004 \| 0.7470 \| 0.747 \| \| 0.4528 \| 59.06 \| 8800 \| 0.5026 \| 0.7459 \| 0.746 \| \| 0.4547 \| 60.4 \| 9000 \| 0.5034 \| 0.7458 \| 0.746 \| \| 0.4547 \| 61.74 \| 9200 \| 0.5012 \| 0.7459 \| 0.746 \| \| 0.4584 \| 63.09 \| 9400 \| 0.5009 \| 0.7459 \| 0.746 \| \| 0.4507 \| 64.43 \| 9600 \| 0.5012 \| 0.7489 \| 0.749 \| \| 0.4539 \| 65.77 \| 9800 \| 0.5020 \| 0.7469 \| 0.747 \| \| 0.4504 \| 67.11 \| 10000 \| 0.5006 \| 0.7470 \| 0.747 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_tf_2-seqsight_4096_512_15M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_tf_2-seqsight_4096_512_15M-L8_f	null	[ "region:us" ]	null	2024-05-03T19:14:29+00:00	[]	[]	TAGS #region-us	GUE\_tf\_2-seqsight\_4096\_512\_15M-L8\_f ========================================= This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_tf\_2 dataset. It achieves the following results on the evaluation set: * Loss: 0.4762 * F1 Score: 0.7889 * Accuracy: 0.789 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]	[ "TAGS\n#region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]	[ 5, 100, 5, 52 ]	[ "TAGS\n#region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000### Training results### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]
null	null	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # GUE_tf_2-seqsight_4096_512_15M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_tf_2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_2) dataset. It achieves the following results on the evaluation set: - Loss: 0.4798 - F1 Score: 0.7869 - Accuracy: 0.787 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.578 \| 1.34 \| 200 \| 0.5342 \| 0.7303 \| 0.733 \| \| 0.531 \| 2.68 \| 400 \| 0.5274 \| 0.7439 \| 0.745 \| \| 0.5177 \| 4.03 \| 600 \| 0.5157 \| 0.7400 \| 0.74 \| \| 0.5099 \| 5.37 \| 800 \| 0.5128 \| 0.7489 \| 0.749 \| \| 0.5048 \| 6.71 \| 1000 \| 0.5149 \| 0.7448 \| 0.745 \| \| 0.4968 \| 8.05 \| 1200 \| 0.5041 \| 0.7375 \| 0.738 \| \| 0.4897 \| 9.4 \| 1400 \| 0.5042 \| 0.7520 \| 0.752 \| \| 0.486 \| 10.74 \| 1600 \| 0.5024 \| 0.7480 \| 0.748 \| \| 0.4817 \| 12.08 \| 1800 \| 0.5059 \| 0.7574 \| 0.758 \| \| 0.4755 \| 13.42 \| 2000 \| 0.5121 \| 0.7437 \| 0.744 \| \| 0.4763 \| 14.77 \| 2200 \| 0.5078 \| 0.7339 \| 0.736 \| \| 0.4663 \| 16.11 \| 2400 \| 0.5129 \| 0.7577 \| 0.758 \| \| 0.4685 \| 17.45 \| 2600 \| 0.5037 \| 0.7478 \| 0.748 \| \| 0.4603 \| 18.79 \| 2800 \| 0.4975 \| 0.7444 \| 0.745 \| \| 0.4557 \| 20.13 \| 3000 \| 0.5109 \| 0.7469 \| 0.747 \| \| 0.4502 \| 21.48 \| 3200 \| 0.5222 \| 0.7300 \| 0.731 \| \| 0.4525 \| 22.82 \| 3400 \| 0.5181 \| 0.7539 \| 0.754 \| \| 0.4457 \| 24.16 \| 3600 \| 0.5046 \| 0.7480 \| 0.748 \| \| 0.4382 \| 25.5 \| 3800 \| 0.5103 \| 0.7479 \| 0.748 \| \| 0.4378 \| 26.85 \| 4000 \| 0.5076 \| 0.7479 \| 0.748 \| \| 0.4323 \| 28.19 \| 4200 \| 0.5127 \| 0.7404 \| 0.741 \| \| 0.4281 \| 29.53 \| 4400 \| 0.5187 \| 0.7369 \| 0.737 \| \| 0.4288 \| 30.87 \| 4600 \| 0.5104 \| 0.7460 \| 0.746 \| \| 0.4232 \| 32.21 \| 4800 \| 0.5187 \| 0.7560 \| 0.756 \| \| 0.4203 \| 33.56 \| 5000 \| 0.5202 \| 0.7537 \| 0.754 \| \| 0.4205 \| 34.9 \| 5200 \| 0.5271 \| 0.7454 \| 0.746 \| \| 0.409 \| 36.24 \| 5400 \| 0.5216 \| 0.7489 \| 0.749 \| \| 0.4114 \| 37.58 \| 5600 \| 0.5241 \| 0.7477 \| 0.748 \| \| 0.4077 \| 38.93 \| 5800 \| 0.5173 \| 0.7479 \| 0.748 \| \| 0.404 \| 40.27 \| 6000 \| 0.5202 \| 0.7560 \| 0.756 \| \| 0.4026 \| 41.61 \| 6200 \| 0.5207 \| 0.7430 \| 0.743 \| \| 0.3983 \| 42.95 \| 6400 \| 0.5391 \| 0.7477 \| 0.748 \| \| 0.3954 \| 44.3 \| 6600 \| 0.5431 \| 0.7377 \| 0.738 \| \| 0.3973 \| 45.64 \| 6800 \| 0.5416 \| 0.7351 \| 0.736 \| \| 0.3911 \| 46.98 \| 7000 \| 0.5404 \| 0.7419 \| 0.742 \| \| 0.3916 \| 48.32 \| 7200 \| 0.5340 \| 0.7429 \| 0.743 \| \| 0.3874 \| 49.66 \| 7400 \| 0.5330 \| 0.7450 \| 0.745 \| \| 0.3831 \| 51.01 \| 7600 \| 0.5419 \| 0.7387 \| 0.739 \| \| 0.3811 \| 52.35 \| 7800 \| 0.5460 \| 0.7430 \| 0.743 \| \| 0.3823 \| 53.69 \| 8000 \| 0.5400 \| 0.7440 \| 0.744 \| \| 0.3795 \| 55.03 \| 8200 \| 0.5479 \| 0.7407 \| 0.741 \| \| 0.3828 \| 56.38 \| 8400 \| 0.5518 \| 0.7407 \| 0.741 \| \| 0.379 \| 57.72 \| 8600 \| 0.5405 \| 0.7458 \| 0.746 \| \| 0.3751 \| 59.06 \| 8800 \| 0.5438 \| 0.7388 \| 0.739 \| \| 0.3759 \| 60.4 \| 9000 \| 0.5491 \| 0.7407 \| 0.741 \| \| 0.3729 \| 61.74 \| 9200 \| 0.5489 \| 0.7458 \| 0.746 \| \| 0.3759 \| 63.09 \| 9400 \| 0.5501 \| 0.7437 \| 0.744 \| \| 0.3732 \| 64.43 \| 9600 \| 0.5483 \| 0.7388 \| 0.739 \| \| 0.375 \| 65.77 \| 9800 \| 0.5503 \| 0.7446 \| 0.745 \| \| 0.369 \| 67.11 \| 10000 \| 0.5488 \| 0.7369 \| 0.737 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_tf_2-seqsight_4096_512_15M-L32_f", "results": []}]}	mahdibaghbanzadeh/GUE_tf_2-seqsight_4096_512_15M-L32_f	null	[ "region:us" ]	null	2024-05-03T19:15:29+00:00	[]	[]	TAGS #region-us	GUE\_tf\_2-seqsight\_4096\_512\_15M-L32\_f ========================================== This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_tf\_2 dataset. It achieves the following results on the evaluation set: * Loss: 0.4798 * F1 Score: 0.7869 * Accuracy: 0.787 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]	[ "TAGS\n#region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]	[ 5, 100, 5, 52 ]	[ "TAGS\n#region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000### Training results### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]
null	null	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # GUE_virus_covid-seqsight_4096_512_15M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_virus_covid](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_virus_covid) dataset. It achieves the following results on the evaluation set: - Loss: 1.9282 - F1 Score: 0.2779 - Accuracy: 0.2832 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 2.1857 \| 0.35 \| 200 \| 2.1860 \| 0.0716 \| 0.1278 \| \| 2.183 \| 0.7 \| 400 \| 2.1840 \| 0.0572 \| 0.1247 \| \| 2.1793 \| 1.05 \| 600 \| 2.1784 \| 0.0832 \| 0.1352 \| \| 2.1765 \| 1.4 \| 800 \| 2.1736 \| 0.0908 \| 0.1396 \| \| 2.1694 \| 1.75 \| 1000 \| 2.1685 \| 0.1077 \| 0.1492 \| \| 2.1674 \| 2.09 \| 1200 \| 2.1739 \| 0.0989 \| 0.1451 \| \| 2.1644 \| 2.44 \| 1400 \| 2.1706 \| 0.1217 \| 0.1480 \| \| 2.1612 \| 2.79 \| 1600 \| 2.1591 \| 0.1324 \| 0.1615 \| \| 2.1568 \| 3.14 \| 1800 \| 2.1538 \| 0.1270 \| 0.1687 \| \| 2.1522 \| 3.49 \| 2000 \| 2.1578 \| 0.1333 \| 0.1689 \| \| 2.1535 \| 3.84 \| 2200 \| 2.1464 \| 0.1515 \| 0.1814 \| \| 2.1446 \| 4.19 \| 2400 \| 2.1428 \| 0.1506 \| 0.1716 \| \| 2.1418 \| 4.54 \| 2600 \| 2.1369 \| 0.1560 \| 0.1859 \| \| 2.138 \| 4.89 \| 2800 \| 2.1304 \| 0.1727 \| 0.1887 \| \| 2.133 \| 5.24 \| 3000 \| 2.1352 \| 0.1610 \| 0.1908 \| \| 2.1303 \| 5.58 \| 3200 \| 2.1227 \| 0.1787 \| 0.2040 \| \| 2.127 \| 5.93 \| 3400 \| 2.1300 \| 0.1451 \| 0.1809 \| \| 2.121 \| 6.28 \| 3600 \| 2.1118 \| 0.1827 \| 0.2046 \| \| 2.1132 \| 6.63 \| 3800 \| 2.0989 \| 0.1781 \| 0.2027 \| \| 2.11 \| 6.98 \| 4000 \| 2.0828 \| 0.2078 \| 0.2254 \| \| 2.0955 \| 7.33 \| 4200 \| 2.0556 \| 0.2196 \| 0.2338 \| \| 2.0834 \| 7.68 \| 4400 \| 2.0488 \| 0.2224 \| 0.2342 \| \| 2.0747 \| 8.03 \| 4600 \| 2.0685 \| 0.1803 \| 0.2083 \| \| 2.0662 \| 8.38 \| 4800 \| 2.0344 \| 0.2150 \| 0.2323 \| \| 2.0627 \| 8.73 \| 5000 \| 2.0267 \| 0.2107 \| 0.2333 \| \| 2.0541 \| 9.08 \| 5200 \| 2.0213 \| 0.2244 \| 0.2355 \| \| 2.0482 \| 9.42 \| 5400 \| 2.0056 \| 0.2347 \| 0.2490 \| \| 2.0413 \| 9.77 \| 5600 \| 2.0041 \| 0.2293 \| 0.2441 \| \| 2.0395 \| 10.12 \| 5800 \| 1.9909 \| 0.2505 \| 0.2573 \| \| 2.0322 \| 10.47 \| 6000 \| 1.9841 \| 0.2563 \| 0.2616 \| \| 2.0275 \| 10.82 \| 6200 \| 1.9875 \| 0.2414 \| 0.2515 \| \| 2.0227 \| 11.17 \| 6400 \| 1.9840 \| 0.2401 \| 0.2509 \| \| 2.0205 \| 11.52 \| 6600 \| 1.9861 \| 0.2374 \| 0.2514 \| \| 2.0191 \| 11.87 \| 6800 \| 1.9717 \| 0.2484 \| 0.2594 \| \| 2.0118 \| 12.22 \| 7000 \| 1.9615 \| 0.2657 \| 0.2700 \| \| 2.008 \| 12.57 \| 7200 \| 1.9528 \| 0.2658 \| 0.2708 \| \| 2.0108 \| 12.91 \| 7400 \| 1.9626 \| 0.2555 \| 0.2638 \| \| 2.0043 \| 13.26 \| 7600 \| 1.9508 \| 0.2567 \| 0.2681 \| \| 1.9972 \| 13.61 \| 7800 \| 1.9566 \| 0.2538 \| 0.2635 \| \| 1.9999 \| 13.96 \| 8000 \| 1.9473 \| 0.2719 \| 0.2755 \| \| 1.9947 \| 14.31 \| 8200 \| 1.9432 \| 0.2678 \| 0.2758 \| \| 1.9987 \| 14.66 \| 8400 \| 1.9337 \| 0.2747 \| 0.2785 \| \| 1.9902 \| 15.01 \| 8600 \| 1.9422 \| 0.2650 \| 0.2717 \| \| 1.9921 \| 15.36 \| 8800 \| 1.9332 \| 0.2762 \| 0.2783 \| \| 1.9841 \| 15.71 \| 9000 \| 1.9405 \| 0.2699 \| 0.2780 \| \| 1.9876 \| 16.06 \| 9200 \| 1.9298 \| 0.2772 \| 0.2806 \| \| 1.9878 \| 16.4 \| 9400 \| 1.9299 \| 0.2749 \| 0.2798 \| \| 1.9869 \| 16.75 \| 9600 \| 1.9348 \| 0.2755 \| 0.2804 \| \| 1.9865 \| 17.1 \| 9800 \| 1.9314 \| 0.2739 \| 0.2793 \| \| 1.9921 \| 17.45 \| 10000 \| 1.9304 \| 0.2764 \| 0.2804 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_virus_covid-seqsight_4096_512_15M-L1_f", "results": []}]}	mahdibaghbanzadeh/GUE_virus_covid-seqsight_4096_512_15M-L1_f	null	[ "region:us" ]	null	2024-05-03T19:16:42+00:00	[]	[]	TAGS #region-us	GUE\_virus\_covid-seqsight\_4096\_512\_15M-L1\_f ================================================ This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_virus\_covid dataset. It achieves the following results on the evaluation set: * Loss: 1.9282 * F1 Score: 0.2779 * Accuracy: 0.2832 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]	[ "TAGS\n#region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]	[ 5, 100, 5, 52 ]	[ "TAGS\n#region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000### Training results### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]
null	null	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 5, 6, 4, 75, 23, 3, 5, 8, 9, 8, 34, 20, 4, 5, 5, 11, 13, 12, 3, 10, 6, 5, 6, 4, 5, 7, 49, 7, 7, 5, 5, 15, 7, 7, 8, 5 ]	[ "TAGS\n#region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. 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null	null	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # GUE_virus_covid-seqsight_4096_512_15M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_virus_covid](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_virus_covid) dataset. It achieves the following results on the evaluation set: - Loss: 1.5376 - F1 Score: 0.4255 - Accuracy: 0.4204 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 2.1853 \| 0.35 \| 200 \| 2.1837 \| 0.0874 \| 0.1342 \| \| 2.1805 \| 0.7 \| 400 \| 2.1771 \| 0.0925 \| 0.1295 \| \| 2.1715 \| 1.05 \| 600 \| 2.1691 \| 0.1098 \| 0.1369 \| \| 2.1617 \| 1.4 \| 800 \| 2.1614 \| 0.1110 \| 0.1502 \| \| 2.1494 \| 1.75 \| 1000 \| 2.1453 \| 0.1504 \| 0.1804 \| \| 2.1399 \| 2.09 \| 1200 \| 2.1424 \| 0.1226 \| 0.1747 \| \| 2.11 \| 2.44 \| 1400 \| 2.0699 \| 0.1890 \| 0.2135 \| \| 2.0694 \| 2.79 \| 1600 \| 2.0231 \| 0.2130 \| 0.2406 \| \| 2.0288 \| 3.14 \| 1800 \| 2.0134 \| 0.2062 \| 0.2318 \| \| 1.9962 \| 3.49 \| 2000 \| 1.9502 \| 0.2475 \| 0.2598 \| \| 1.9712 \| 3.84 \| 2200 \| 1.8961 \| 0.2710 \| 0.2816 \| \| 1.9382 \| 4.19 \| 2400 \| 1.8577 \| 0.2936 \| 0.2901 \| \| 1.9121 \| 4.54 \| 2600 \| 1.8328 \| 0.3132 \| 0.3178 \| \| 1.8976 \| 4.89 \| 2800 \| 1.8175 \| 0.3134 \| 0.3129 \| \| 1.875 \| 5.24 \| 3000 \| 1.7826 \| 0.3280 \| 0.3340 \| \| 1.8617 \| 5.58 \| 3200 \| 1.7518 \| 0.3499 \| 0.3488 \| \| 1.8365 \| 5.93 \| 3400 \| 1.7553 \| 0.3296 \| 0.3388 \| \| 1.8209 \| 6.28 \| 3600 \| 1.7260 \| 0.3515 \| 0.3516 \| \| 1.8059 \| 6.63 \| 3800 \| 1.7081 \| 0.3620 \| 0.3599 \| \| 1.8003 \| 6.98 \| 4000 \| 1.7012 \| 0.3732 \| 0.3702 \| \| 1.7834 \| 7.33 \| 4200 \| 1.6943 \| 0.3664 \| 0.3658 \| \| 1.7706 \| 7.68 \| 4400 \| 1.6790 \| 0.3783 \| 0.3660 \| \| 1.767 \| 8.03 \| 4600 \| 1.6793 \| 0.3684 \| 0.3688 \| \| 1.7547 \| 8.38 \| 4800 \| 1.6680 \| 0.3748 \| 0.3752 \| \| 1.7509 \| 8.73 \| 5000 \| 1.6592 \| 0.3763 \| 0.3802 \| \| 1.7496 \| 9.08 \| 5200 \| 1.6561 \| 0.3869 \| 0.3803 \| \| 1.7273 \| 9.42 \| 5400 \| 1.6421 \| 0.3869 \| 0.3880 \| \| 1.7283 \| 9.77 \| 5600 \| 1.6331 \| 0.3979 \| 0.3955 \| \| 1.725 \| 10.12 \| 5800 \| 1.6186 \| 0.4024 \| 0.3932 \| \| 1.7221 \| 10.47 \| 6000 \| 1.6145 \| 0.3986 \| 0.3946 \| \| 1.7101 \| 10.82 \| 6200 \| 1.6078 \| 0.4082 \| 0.4012 \| \| 1.6922 \| 11.17 \| 6400 \| 1.6023 \| 0.4073 \| 0.4024 \| \| 1.6973 \| 11.52 \| 6600 \| 1.5917 \| 0.4116 \| 0.4045 \| \| 1.6989 \| 11.87 \| 6800 \| 1.5862 \| 0.4106 \| 0.4053 \| \| 1.684 \| 12.22 \| 7000 \| 1.5780 \| 0.4176 \| 0.4108 \| \| 1.674 \| 12.57 \| 7200 \| 1.5750 \| 0.4172 \| 0.4123 \| \| 1.6799 \| 12.91 \| 7400 \| 1.5693 \| 0.4194 \| 0.4140 \| \| 1.6687 \| 13.26 \| 7600 \| 1.5574 \| 0.4183 \| 0.4153 \| \| 1.6716 \| 13.61 \| 7800 \| 1.5663 \| 0.4222 \| 0.4162 \| \| 1.6615 \| 13.96 \| 8000 \| 1.5567 \| 0.4226 \| 0.4177 \| \| 1.6562 \| 14.31 \| 8200 \| 1.5533 \| 0.4217 \| 0.4166 \| \| 1.6584 \| 14.66 \| 8400 \| 1.5481 \| 0.4290 \| 0.4196 \| \| 1.656 \| 15.01 \| 8600 \| 1.5455 \| 0.4272 \| 0.4237 \| \| 1.6563 \| 15.36 \| 8800 \| 1.5480 \| 0.4297 \| 0.4204 \| \| 1.639 \| 15.71 \| 9000 \| 1.5463 \| 0.4260 \| 0.4224 \| \| 1.6507 \| 16.06 \| 9200 \| 1.5438 \| 0.4242 \| 0.4192 \| \| 1.6477 \| 16.4 \| 9400 \| 1.5385 \| 0.4275 \| 0.4226 \| \| 1.6475 \| 16.75 \| 9600 \| 1.5404 \| 0.4289 \| 0.4243 \| \| 1.6414 \| 17.1 \| 9800 \| 1.5406 \| 0.4294 \| 0.4249 \| \| 1.6511 \| 17.45 \| 10000 \| 1.5388 \| 0.4300 \| 0.4249 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_virus_covid-seqsight_4096_512_15M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_virus_covid-seqsight_4096_512_15M-L8_f	null	[ "region:us" ]	null	2024-05-03T19:16:59+00:00	[]	[]	TAGS #region-us	GUE\_virus\_covid-seqsight\_4096\_512\_15M-L8\_f ================================================ This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_virus\_covid dataset. It achieves the following results on the evaluation set: * Loss: 1.5376 * F1 Score: 0.4255 * Accuracy: 0.4204 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]	[ "TAGS\n#region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]	[ 5, 100, 5, 52 ]	[ "TAGS\n#region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000### Training results### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

text-generation

transformers

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) sqlcoder-7b-2 - bnb 4bits - Model creator: https://huggingface.co/defog/ - Original model: https://huggingface.co/defog/sqlcoder-7b-2/ Original model description: --- license: cc-by-sa-4.0 library_name: transformers pipeline_tag: text-generation --- # Update notice The model weights were updated at 7 AM UTC on Feb 7, 2024. The new model weights lead to a much more performant model – particularly for joins. If you downloaded the model before that, please redownload the weights for best performance. # Model Card for SQLCoder-7B-2 A capable large language model for natural language to SQL generation. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/603bbad3fd770a9997b57cb6/AYUE2y14vy2XkD9MZpScu.png) ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [Defog, Inc](https://defog.ai) - **Model type:** [Text to SQL] - **License:** [CC-by-SA-4.0] - **Finetuned from model:** [CodeLlama-7B] ### Model Sources [optional] - [**HuggingFace:**](https://huggingface.co/defog/sqlcoder-70b-alpha) - [**GitHub:**](https://github.com/defog-ai/sqlcoder) - [**Demo:**](https://defog.ai/sqlcoder-demo/) ## Uses This model is intended to be used by non-technical users to understand data inside their SQL databases. It is meant as an analytics tool, and not as a database admin tool. This model has not been trained to reject malicious requests from users with write access to databases, and should only be used by users with read-only access. ## How to Get Started with the Model Use the code [here](https://github.com/defog-ai/sqlcoder/blob/main/inference.py) to get started with the model. ## Prompt Please use the following prompt for optimal results. Please remember to use `do_sample=False` and `num_beams=4` for optimal results. ``` ### Task Generate a SQL query to answer [QUESTION]{user_question}[/QUESTION] ### Database Schema The query will run on a database with the following schema: {table_metadata_string_DDL_statements} ### Answer Given the database schema, here is the SQL query that [QUESTION]{user_question}[/QUESTION] [SQL] ``` ## Evaluation This model was evaluated on [SQL-Eval](https://github.com/defog-ai/sql-eval), a PostgreSQL based evaluation framework developed by Defog for testing and alignment of model capabilities. You can read more about the methodology behind SQLEval [here](https://defog.ai/blog/open-sourcing-sqleval/). ### Results We classified each generated question into one of 6 categories. The table displays the percentage of questions answered correctly by each model, broken down by category. | | date | group_by | order_by | ratio | join | where | | -------------- | ---- | -------- | -------- | ----- | ---- | ----- | | sqlcoder-70b | 96 | 91.4 | 97.1 | 85.7 | 97.1 | 91.4 | | sqlcoder-7b-2 | 96 | 91.4 | 94.3 | 91.4 | 94.3 | 77.1 | | sqlcoder-34b | 80 | 94.3 | 85.7 | 77.1 | 85.7 | 80 | | gpt-4 | 72 | 94.3 | 97.1 | 80 | 91.4 | 80 | | gpt-4-turbo | 76 | 91.4 | 91.4 | 62.8 | 88.6 | 77.1 | | natural-sql-7b | 56 | 88.6 | 85.7 | 60 | 88.6 | 80 | | sqlcoder-7b | 64 | 82.9 | 74.3 | 54.3 | 74.3 | 74.3 | | gpt-3.5 | 72 | 77.1 | 82.8 | 34.3 | 65.7 | 71.4 | | claude-2 | 52 | 71.4 | 74.3 | 57.1 | 65.7 | 62.9 | ## Model Card Contact Contact us on X at [@defogdata](https://twitter.com/defogdata), or on email at [founders@defog.ai](mailto:founders@defog.ai)

{}

RichardErkhov/defog_-_sqlcoder-7b-2-4bits

null

[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]

null

2024-05-03T19:09:04+00:00

[]

TAGS #transformers #safetensors #llama #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us

Quantization made by Richard Erkhov. Github Discord Request more models sqlcoder-7b-2 - bnb 4bits * Model creator: URL * Original model: URL Original model description: --------------------------- license: cc-by-sa-4.0 library\_name: transformers pipeline\_tag: text-generation -------------------------------------------------------------------------------- Update notice ============= The model weights were updated at 7 AM UTC on Feb 7, 2024. The new model weights lead to a much more performant model – particularly for joins. If you downloaded the model before that, please redownload the weights for best performance. Model Card for SQLCoder-7B-2 ============================ A capable large language model for natural language to SQL generation. !image/png Model Details ------------- ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. * Developed by: Defog, Inc * Model type: [Text to SQL] * License: [CC-by-SA-4.0] * Finetuned from model: [CodeLlama-7B] ### Model Sources [optional] * HuggingFace: * GitHub: * Demo: Uses ---- This model is intended to be used by non-technical users to understand data inside their SQL databases. It is meant as an analytics tool, and not as a database admin tool. This model has not been trained to reject malicious requests from users with write access to databases, and should only be used by users with read-only access. How to Get Started with the Model --------------------------------- Use the code here to get started with the model. Prompt ------ Please use the following prompt for optimal results. Please remember to use 'do\_sample=False' and 'num\_beams=4' for optimal results. Evaluation ---------- This model was evaluated on SQL-Eval, a PostgreSQL based evaluation framework developed by Defog for testing and alignment of model capabilities. You can read more about the methodology behind SQLEval here. ### Results We classified each generated question into one of 6 categories. The table displays the percentage of questions answered correctly by each model, broken down by category. Model Card Contact ------------------ Contact us on X at @defogdata, or on email at founders@URL

[ "### Model Description\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n\n* Developed by: Defog, Inc\n* Model type: [Text to SQL]\n* License: [CC-by-SA-4.0]\n* Finetuned from model: [CodeLlama-7B]", "### Model Sources [optional]\n\n\n* HuggingFace:\n* GitHub:\n* Demo:\n\n\nUses\n----\n\n\nThis model is intended to be used by non-technical users to understand data inside their SQL databases. It is meant as an analytics tool, and not as a database admin tool.\n\n\nThis model has not been trained to reject malicious requests from users with write access to databases, and should only be used by users with read-only access.\n\n\nHow to Get Started with the Model\n---------------------------------\n\n\nUse the code here to get started with the model.\n\n\nPrompt\n------\n\n\nPlease use the following prompt for optimal results. Please remember to use 'do\\_sample=False' and 'num\\_beams=4' for optimal results.\n\n\nEvaluation\n----------\n\n\nThis model was evaluated on SQL-Eval, a PostgreSQL based evaluation framework developed by Defog for testing and alignment of model capabilities.\n\n\nYou can read more about the methodology behind SQLEval here.", "### Results\n\n\nWe classified each generated question into one of 6 categories. The table displays the percentage of questions answered correctly by each model, broken down by category.\n\n\n\nModel Card Contact\n------------------\n\n\nContact us on X at @defogdata, or on email at founders@URL" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "### Model Description\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n\n* Developed by: Defog, Inc\n* Model type: [Text to SQL]\n* License: [CC-by-SA-4.0]\n* Finetuned from model: [CodeLlama-7B]", "### Model Sources [optional]\n\n\n* HuggingFace:\n* GitHub:\n* Demo:\n\n\nUses\n----\n\n\nThis model is intended to be used by non-technical users to understand data inside their SQL databases. It is meant as an analytics tool, and not as a database admin tool.\n\n\nThis model has not been trained to reject malicious requests from users with write access to databases, and should only be used by users with read-only access.\n\n\nHow to Get Started with the Model\n---------------------------------\n\n\nUse the code here to get started with the model.\n\n\nPrompt\n------\n\n\nPlease use the following prompt for optimal results. Please remember to use 'do\\_sample=False' and 'num\\_beams=4' for optimal results.\n\n\nEvaluation\n----------\n\n\nThis model was evaluated on SQL-Eval, a PostgreSQL based evaluation framework developed by Defog for testing and alignment of model capabilities.\n\n\nYou can read more about the methodology behind SQLEval here.", "### Results\n\n\nWe classified each generated question into one of 6 categories. The table displays the percentage of questions answered correctly by each model, broken down by category.\n\n\n\nModel Card Contact\n------------------\n\n\nContact us on X at @defogdata, or on email at founders@URL" ]

[ 38, 76, 241, 73 ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n### Model Description\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n\n* Developed by: Defog, Inc\n* Model type: [Text to SQL]\n* License: [CC-by-SA-4.0]\n* Finetuned from model: [CodeLlama-7B]### Model Sources [optional]\n\n\n* HuggingFace:\n* GitHub:\n* Demo:\n\n\nUses\n----\n\n\nThis model is intended to be used by non-technical users to understand data inside their SQL databases. It is meant as an analytics tool, and not as a database admin tool.\n\n\nThis model has not been trained to reject malicious requests from users with write access to databases, and should only be used by users with read-only access.\n\n\nHow to Get Started with the Model\n---------------------------------\n\n\nUse the code here to get started with the model.\n\n\nPrompt\n------\n\n\nPlease use the following prompt for optimal results. Please remember to use 'do\\_sample=False' and 'num\\_beams=4' for optimal results.\n\n\nEvaluation\n----------\n\n\nThis model was evaluated on SQL-Eval, a PostgreSQL based evaluation framework developed by Defog for testing and alignment of model capabilities.\n\n\nYou can read more about the methodology behind SQLEval here.### Results\n\n\nWe classified each generated question into one of 6 categories. The table displays the percentage of questions answered correctly by each model, broken down by category.\n\n\n\nModel Card Contact\n------------------\n\n\nContact us on X at @defogdata, or on email at founders@URL" ]

null

peft

# GUE_tf_4-seqsight_4096_512_15M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_tf_4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_4) dataset. It achieves the following results on the evaluation set: - Loss: 0.3704 - F1 Score: 0.8588 - Accuracy: 0.859 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.5525 | 1.34 | 200 | 0.5126 | 0.7380 | 0.738 | | 0.4762 | 2.68 | 400 | 0.4937 | 0.7496 | 0.75 | | 0.4601 | 4.03 | 600 | 0.4891 | 0.7548 | 0.756 | | 0.4466 | 5.37 | 800 | 0.4828 | 0.7580 | 0.758 | | 0.4325 | 6.71 | 1000 | 0.4821 | 0.7670 | 0.769 | | 0.4221 | 8.05 | 1200 | 0.4624 | 0.7769 | 0.777 | | 0.416 | 9.4 | 1400 | 0.4501 | 0.7809 | 0.781 | | 0.4062 | 10.74 | 1600 | 0.4531 | 0.7800 | 0.78 | | 0.3994 | 12.08 | 1800 | 0.4526 | 0.7831 | 0.784 | | 0.3951 | 13.42 | 2000 | 0.4485 | 0.7939 | 0.794 | | 0.3826 | 14.77 | 2200 | 0.4444 | 0.7958 | 0.796 | | 0.3825 | 16.11 | 2400 | 0.4407 | 0.7955 | 0.796 | | 0.3734 | 17.45 | 2600 | 0.4475 | 0.7848 | 0.785 | | 0.367 | 18.79 | 2800 | 0.4480 | 0.7940 | 0.794 | | 0.3628 | 20.13 | 3000 | 0.4385 | 0.8019 | 0.802 | | 0.3505 | 21.48 | 3200 | 0.4360 | 0.8079 | 0.808 | | 0.3513 | 22.82 | 3400 | 0.4419 | 0.8037 | 0.804 | | 0.345 | 24.16 | 3600 | 0.4359 | 0.8080 | 0.808 | | 0.3405 | 25.5 | 3800 | 0.4313 | 0.8097 | 0.81 | | 0.3327 | 26.85 | 4000 | 0.4307 | 0.8130 | 0.813 | | 0.3347 | 28.19 | 4200 | 0.4333 | 0.7970 | 0.797 | | 0.319 | 29.53 | 4400 | 0.4489 | 0.8188 | 0.819 | | 0.3213 | 30.87 | 4600 | 0.4355 | 0.8050 | 0.805 | | 0.3171 | 32.21 | 4800 | 0.4279 | 0.8090 | 0.809 | | 0.3143 | 33.56 | 5000 | 0.4330 | 0.8120 | 0.812 | | 0.3113 | 34.9 | 5200 | 0.4400 | 0.8070 | 0.807 | | 0.3048 | 36.24 | 5400 | 0.4414 | 0.798 | 0.798 | | 0.2986 | 37.58 | 5600 | 0.4316 | 0.8146 | 0.815 | | 0.295 | 38.93 | 5800 | 0.4465 | 0.8040 | 0.804 | | 0.295 | 40.27 | 6000 | 0.4404 | 0.8098 | 0.81 | | 0.2883 | 41.61 | 6200 | 0.4515 | 0.8090 | 0.809 | | 0.2897 | 42.95 | 6400 | 0.4408 | 0.8110 | 0.811 | | 0.2857 | 44.3 | 6600 | 0.4365 | 0.8145 | 0.815 | | 0.2787 | 45.64 | 6800 | 0.4331 | 0.8120 | 0.812 | | 0.2862 | 46.98 | 7000 | 0.4335 | 0.8189 | 0.819 | | 0.2767 | 48.32 | 7200 | 0.4339 | 0.8148 | 0.815 | | 0.2712 | 49.66 | 7400 | 0.4270 | 0.8129 | 0.813 | | 0.2712 | 51.01 | 7600 | 0.4322 | 0.8170 | 0.817 | | 0.2708 | 52.35 | 7800 | 0.4382 | 0.8198 | 0.82 | | 0.2644 | 53.69 | 8000 | 0.4400 | 0.8160 | 0.816 | | 0.2678 | 55.03 | 8200 | 0.4366 | 0.8230 | 0.823 | | 0.2635 | 56.38 | 8400 | 0.4318 | 0.8229 | 0.823 | | 0.261 | 57.72 | 8600 | 0.4403 | 0.8178 | 0.818 | | 0.262 | 59.06 | 8800 | 0.4338 | 0.8179 | 0.818 | | 0.2617 | 60.4 | 9000 | 0.4364 | 0.8220 | 0.822 | | 0.2545 | 61.74 | 9200 | 0.4385 | 0.8219 | 0.822 | | 0.2568 | 63.09 | 9400 | 0.4400 | 0.8289 | 0.829 | | 0.257 | 64.43 | 9600 | 0.4372 | 0.8239 | 0.824 | | 0.2581 | 65.77 | 9800 | 0.4372 | 0.8249 | 0.825 | | 0.2546 | 67.11 | 10000 | 0.4370 | 0.8259 | 0.826 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_tf_4-seqsight_4096_512_15M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_tf_4-seqsight_4096_512_15M-L32_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]

null

2024-05-03T19:09:48+00:00

[]

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us

GUE\_tf\_4-seqsight\_4096\_512\_15M-L32\_f ========================================== This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_tf\_4 dataset. It achieves the following results on the evaluation set: * Loss: 0.3704 * F1 Score: 0.8588 * Accuracy: 0.859 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ 43, 100, 5, 52 ]

[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000### Training results### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

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peft

# GUE_tf_3-seqsight_4096_512_15M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_tf_3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_3) dataset. It achieves the following results on the evaluation set: - Loss: 0.5670 - F1 Score: 0.6927 - Accuracy: 0.696 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6616 | 0.93 | 200 | 0.6000 | 0.6809 | 0.682 | | 0.618 | 1.87 | 400 | 0.5894 | 0.6799 | 0.68 | | 0.6049 | 2.8 | 600 | 0.5722 | 0.7073 | 0.711 | | 0.6017 | 3.74 | 800 | 0.5692 | 0.7085 | 0.71 | | 0.5991 | 4.67 | 1000 | 0.5638 | 0.7147 | 0.716 | | 0.5926 | 5.61 | 1200 | 0.5632 | 0.7197 | 0.72 | | 0.5904 | 6.54 | 1400 | 0.5591 | 0.7143 | 0.716 | | 0.5889 | 7.48 | 1600 | 0.5586 | 0.7229 | 0.723 | | 0.5877 | 8.41 | 1800 | 0.5571 | 0.7163 | 0.717 | | 0.59 | 9.35 | 2000 | 0.5569 | 0.7177 | 0.719 | | 0.5839 | 10.28 | 2200 | 0.5590 | 0.7100 | 0.71 | | 0.5842 | 11.21 | 2400 | 0.5519 | 0.7183 | 0.72 | | 0.5841 | 12.15 | 2600 | 0.5506 | 0.7176 | 0.721 | | 0.581 | 13.08 | 2800 | 0.5494 | 0.7161 | 0.719 | | 0.5822 | 14.02 | 3000 | 0.5530 | 0.7166 | 0.717 | | 0.5808 | 14.95 | 3200 | 0.5503 | 0.7212 | 0.722 | | 0.5786 | 15.89 | 3400 | 0.5493 | 0.7234 | 0.725 | | 0.5755 | 16.82 | 3600 | 0.5515 | 0.7176 | 0.718 | | 0.5761 | 17.76 | 3800 | 0.5495 | 0.7271 | 0.729 | | 0.5766 | 18.69 | 4000 | 0.5525 | 0.7197 | 0.72 | | 0.5732 | 19.63 | 4200 | 0.5478 | 0.7169 | 0.721 | | 0.5766 | 20.56 | 4400 | 0.5462 | 0.7184 | 0.72 | | 0.5746 | 21.5 | 4600 | 0.5500 | 0.7120 | 0.712 | | 0.5734 | 22.43 | 4800 | 0.5467 | 0.7263 | 0.728 | | 0.5739 | 23.36 | 5000 | 0.5478 | 0.7246 | 0.725 | | 0.5734 | 24.3 | 5200 | 0.5494 | 0.7121 | 0.712 | | 0.5696 | 25.23 | 5400 | 0.5453 | 0.7188 | 0.722 | | 0.5745 | 26.17 | 5600 | 0.5448 | 0.7234 | 0.725 | | 0.568 | 27.1 | 5800 | 0.5439 | 0.7209 | 0.724 | | 0.5682 | 28.04 | 6000 | 0.5437 | 0.7299 | 0.731 | | 0.569 | 28.97 | 6200 | 0.5486 | 0.7161 | 0.716 | | 0.5717 | 29.91 | 6400 | 0.5448 | 0.7316 | 0.733 | | 0.5681 | 30.84 | 6600 | 0.5447 | 0.7337 | 0.735 | | 0.5686 | 31.78 | 6800 | 0.5464 | 0.7217 | 0.722 | | 0.5681 | 32.71 | 7000 | 0.5444 | 0.7319 | 0.733 | | 0.5714 | 33.64 | 7200 | 0.5447 | 0.7315 | 0.733 | | 0.5642 | 34.58 | 7400 | 0.5480 | 0.7131 | 0.713 | | 0.5704 | 35.51 | 7600 | 0.5458 | 0.7226 | 0.723 | | 0.5689 | 36.45 | 7800 | 0.5453 | 0.7246 | 0.725 | | 0.5676 | 37.38 | 8000 | 0.5453 | 0.7236 | 0.724 | | 0.5647 | 38.32 | 8200 | 0.5449 | 0.7317 | 0.733 | | 0.5652 | 39.25 | 8400 | 0.5451 | 0.7284 | 0.729 | | 0.5662 | 40.19 | 8600 | 0.5453 | 0.7284 | 0.729 | | 0.5649 | 41.12 | 8800 | 0.5455 | 0.7275 | 0.728 | | 0.5682 | 42.06 | 9000 | 0.5454 | 0.7285 | 0.729 | | 0.5665 | 42.99 | 9200 | 0.5461 | 0.7217 | 0.722 | | 0.565 | 43.93 | 9400 | 0.5464 | 0.7199 | 0.72 | | 0.5637 | 44.86 | 9600 | 0.5452 | 0.7266 | 0.727 | | 0.5659 | 45.79 | 9800 | 0.5451 | 0.7285 | 0.729 | | 0.562 | 46.73 | 10000 | 0.5452 | 0.7256 | 0.726 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_tf_3-seqsight_4096_512_15M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_tf_3-seqsight_4096_512_15M-L1_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]

null

2024-05-03T19:09:48+00:00

[]

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us

GUE\_tf\_3-seqsight\_4096\_512\_15M-L1\_f ========================================= This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_tf\_3 dataset. It achieves the following results on the evaluation set: * Loss: 0.5670 * F1 Score: 0.6927 * Accuracy: 0.696 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ 43, 100, 5, 52 ]

[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000### Training results### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

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peft

# GUE_tf_3-seqsight_4096_512_15M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_tf_3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_3) dataset. It achieves the following results on the evaluation set: - Loss: 0.5516 - F1 Score: 0.7033 - Accuracy: 0.706 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6433 | 0.93 | 200 | 0.5805 | 0.7011 | 0.701 | | 0.6017 | 1.87 | 400 | 0.5751 | 0.7004 | 0.701 | | 0.594 | 2.8 | 600 | 0.5608 | 0.7085 | 0.711 | | 0.5899 | 3.74 | 800 | 0.5582 | 0.7090 | 0.709 | | 0.5889 | 4.67 | 1000 | 0.5522 | 0.7134 | 0.714 | | 0.5826 | 5.61 | 1200 | 0.5491 | 0.7141 | 0.715 | | 0.5801 | 6.54 | 1400 | 0.5494 | 0.7171 | 0.718 | | 0.5778 | 7.48 | 1600 | 0.5482 | 0.7223 | 0.723 | | 0.5758 | 8.41 | 1800 | 0.5475 | 0.7218 | 0.722 | | 0.5787 | 9.35 | 2000 | 0.5472 | 0.7054 | 0.709 | | 0.5717 | 10.28 | 2200 | 0.5482 | 0.7199 | 0.72 | | 0.5721 | 11.21 | 2400 | 0.5441 | 0.7227 | 0.724 | | 0.5709 | 12.15 | 2600 | 0.5453 | 0.7008 | 0.707 | | 0.5673 | 13.08 | 2800 | 0.5479 | 0.6937 | 0.701 | | 0.5676 | 14.02 | 3000 | 0.5444 | 0.7196 | 0.721 | | 0.5661 | 14.95 | 3200 | 0.5459 | 0.7086 | 0.712 | | 0.5641 | 15.89 | 3400 | 0.5448 | 0.7142 | 0.716 | | 0.5601 | 16.82 | 3600 | 0.5457 | 0.7172 | 0.719 | | 0.5597 | 17.76 | 3800 | 0.5455 | 0.7127 | 0.716 | | 0.5602 | 18.69 | 4000 | 0.5471 | 0.7187 | 0.719 | | 0.558 | 19.63 | 4200 | 0.5495 | 0.7043 | 0.709 | | 0.559 | 20.56 | 4400 | 0.5477 | 0.7125 | 0.716 | | 0.5577 | 21.5 | 4600 | 0.5518 | 0.7161 | 0.716 | | 0.5555 | 22.43 | 4800 | 0.5469 | 0.7103 | 0.714 | | 0.5556 | 23.36 | 5000 | 0.5495 | 0.7171 | 0.717 | | 0.5544 | 24.3 | 5200 | 0.5554 | 0.6955 | 0.696 | | 0.5502 | 25.23 | 5400 | 0.5482 | 0.7157 | 0.719 | | 0.5575 | 26.17 | 5600 | 0.5434 | 0.7264 | 0.728 | | 0.5477 | 27.1 | 5800 | 0.5433 | 0.7174 | 0.719 | | 0.5481 | 28.04 | 6000 | 0.5441 | 0.7282 | 0.73 | | 0.5482 | 28.97 | 6200 | 0.5480 | 0.7231 | 0.723 | | 0.5491 | 29.91 | 6400 | 0.5455 | 0.7245 | 0.727 | | 0.5473 | 30.84 | 6600 | 0.5441 | 0.7217 | 0.723 | | 0.5492 | 31.78 | 6800 | 0.5472 | 0.7217 | 0.722 | | 0.5466 | 32.71 | 7000 | 0.5442 | 0.7272 | 0.728 | | 0.5503 | 33.64 | 7200 | 0.5444 | 0.7283 | 0.73 | | 0.542 | 34.58 | 7400 | 0.5502 | 0.7191 | 0.719 | | 0.5477 | 35.51 | 7600 | 0.5458 | 0.7290 | 0.729 | | 0.5467 | 36.45 | 7800 | 0.5461 | 0.7257 | 0.726 | | 0.5466 | 37.38 | 8000 | 0.5456 | 0.7278 | 0.728 | | 0.5417 | 38.32 | 8200 | 0.5471 | 0.7259 | 0.727 | | 0.5427 | 39.25 | 8400 | 0.5465 | 0.7237 | 0.724 | | 0.5423 | 40.19 | 8600 | 0.5461 | 0.7255 | 0.726 | | 0.5414 | 41.12 | 8800 | 0.5461 | 0.7285 | 0.729 | | 0.5451 | 42.06 | 9000 | 0.5452 | 0.7277 | 0.728 | | 0.5428 | 42.99 | 9200 | 0.5468 | 0.7259 | 0.726 | | 0.541 | 43.93 | 9400 | 0.5469 | 0.7259 | 0.726 | | 0.538 | 44.86 | 9600 | 0.5463 | 0.7257 | 0.726 | | 0.5423 | 45.79 | 9800 | 0.5461 | 0.7293 | 0.73 | | 0.5373 | 46.73 | 10000 | 0.5468 | 0.7248 | 0.725 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_tf_3-seqsight_4096_512_15M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_tf_3-seqsight_4096_512_15M-L8_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]

null

2024-05-03T19:10:15+00:00

[]

TAGS #peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us

GUE\_tf\_3-seqsight\_4096\_512\_15M-L8\_f ========================================= This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_tf\_3 dataset. It achieves the following results on the evaluation set: * Loss: 0.5516 * F1 Score: 0.7033 * Accuracy: 0.706 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ 43, 100, 5, 52 ]

[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_4096_512_15M #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000### Training results### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

null

# GUE_tf_2-seqsight_4096_512_15M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_tf_2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_2) dataset. It achieves the following results on the evaluation set: - Loss: 0.4762 - F1 Score: 0.7710 - Accuracy: 0.771 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6238 | 1.34 | 200 | 0.5599 | 0.7084 | 0.71 | | 0.5565 | 2.68 | 400 | 0.5340 | 0.7329 | 0.733 | | 0.538 | 4.03 | 600 | 0.5271 | 0.7310 | 0.731 | | 0.5338 | 5.37 | 800 | 0.5242 | 0.7350 | 0.735 | | 0.5298 | 6.71 | 1000 | 0.5203 | 0.7365 | 0.737 | | 0.5247 | 8.05 | 1200 | 0.5171 | 0.7490 | 0.749 | | 0.5214 | 9.4 | 1400 | 0.5140 | 0.7415 | 0.742 | | 0.5209 | 10.74 | 1600 | 0.5141 | 0.7418 | 0.742 | | 0.5181 | 12.08 | 1800 | 0.5195 | 0.7438 | 0.744 | | 0.5183 | 13.42 | 2000 | 0.5135 | 0.7440 | 0.744 | | 0.5182 | 14.77 | 2200 | 0.5122 | 0.7470 | 0.747 | | 0.5123 | 16.11 | 2400 | 0.5162 | 0.7407 | 0.741 | | 0.5154 | 17.45 | 2600 | 0.5111 | 0.7399 | 0.74 | | 0.5098 | 18.79 | 2800 | 0.5099 | 0.7400 | 0.74 | | 0.5091 | 20.13 | 3000 | 0.5103 | 0.7400 | 0.74 | | 0.5095 | 21.48 | 3200 | 0.5116 | 0.7359 | 0.736 | | 0.5106 | 22.82 | 3400 | 0.5074 | 0.7399 | 0.74 | | 0.5052 | 24.16 | 3600 | 0.5060 | 0.7358 | 0.736 | | 0.5024 | 25.5 | 3800 | 0.5064 | 0.7342 | 0.735 | | 0.505 | 26.85 | 4000 | 0.5060 | 0.7375 | 0.738 | | 0.5014 | 28.19 | 4200 | 0.5058 | 0.7340 | 0.734 | | 0.5024 | 29.53 | 4400 | 0.5097 | 0.7410 | 0.741 | | 0.5034 | 30.87 | 4600 | 0.5076 | 0.7380 | 0.738 | | 0.5015 | 32.21 | 4800 | 0.5058 | 0.7390 | 0.739 | | 0.5012 | 33.56 | 5000 | 0.5107 | 0.7417 | 0.742 | | 0.5032 | 34.9 | 5200 | 0.5063 | 0.7389 | 0.739 | | 0.4975 | 36.24 | 5400 | 0.5017 | 0.7367 | 0.737 | | 0.4993 | 37.58 | 5600 | 0.5034 | 0.7420 | 0.742 | | 0.4966 | 38.93 | 5800 | 0.5047 | 0.7370 | 0.737 | | 0.497 | 40.27 | 6000 | 0.5033 | 0.7360 | 0.736 | | 0.4973 | 41.61 | 6200 | 0.5028 | 0.7320 | 0.732 | | 0.4951 | 42.95 | 6400 | 0.5043 | 0.7340 | 0.734 | | 0.4949 | 44.3 | 6600 | 0.5056 | 0.7370 | 0.737 | | 0.4977 | 45.64 | 6800 | 0.5057 | 0.7420 | 0.742 | | 0.4943 | 46.98 | 7000 | 0.5042 | 0.7400 | 0.74 | | 0.4949 | 48.32 | 7200 | 0.5059 | 0.7380 | 0.738 | | 0.4923 | 49.66 | 7400 | 0.5017 | 0.7390 | 0.739 | | 0.4941 | 51.01 | 7600 | 0.5031 | 0.7400 | 0.74 | | 0.4942 | 52.35 | 7800 | 0.5022 | 0.7390 | 0.739 | | 0.4957 | 53.69 | 8000 | 0.5019 | 0.7299 | 0.73 | | 0.492 | 55.03 | 8200 | 0.5023 | 0.7410 | 0.741 | | 0.4959 | 56.38 | 8400 | 0.5038 | 0.7400 | 0.74 | | 0.494 | 57.72 | 8600 | 0.5026 | 0.7370 | 0.737 | | 0.4905 | 59.06 | 8800 | 0.5026 | 0.7340 | 0.734 | | 0.4909 | 60.4 | 9000 | 0.5039 | 0.7390 | 0.739 | | 0.4921 | 61.74 | 9200 | 0.5022 | 0.7360 | 0.736 | | 0.4956 | 63.09 | 9400 | 0.5020 | 0.7360 | 0.736 | | 0.4896 | 64.43 | 9600 | 0.5025 | 0.7380 | 0.738 | | 0.4913 | 65.77 | 9800 | 0.5032 | 0.7370 | 0.737 | | 0.4887 | 67.11 | 10000 | 0.5025 | 0.7370 | 0.737 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_tf_2-seqsight_4096_512_15M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_tf_2-seqsight_4096_512_15M-L1_f

null

[ "region:us" ]

null

2024-05-03T19:11:10+00:00

[]

TAGS #region-us

GUE\_tf\_2-seqsight\_4096\_512\_15M-L1\_f ========================================= This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_tf\_2 dataset. It achieves the following results on the evaluation set: * Loss: 0.4762 * F1 Score: 0.7710 * Accuracy: 0.771 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ "TAGS\n#region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ 5, 100, 5, 52 ]

[ "TAGS\n#region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000### Training results### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

null

transformers

# Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

{"library_name": "transformers", "tags": []}

ferrazzipietro/LS_Llama-2-7b-hf_adapters_en.layer1_NoQuant_32_32_0.05_2_5e-05

null

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-05-03T19:11:20+00:00

[ "1910.09700" ]

[]

TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us

# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ 26, 6, 4, 75, 23, 3, 5, 8, 9, 8, 34, 20, 4, 5, 5, 11, 13, 12, 3, 10, 6, 5, 6, 4, 5, 7, 49, 7, 7, 5, 5, 15, 7, 7, 8, 5 ]

[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]

null

transformers

# Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

{"library_name": "transformers", "tags": []}

Armandodelca/Prototipo_7_EMI

null

[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-05-03T19:12:51+00:00

[ "1910.09700" ]

[]

TAGS #transformers #arxiv-1910.09700 #endpoints_compatible #region-us

# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "TAGS\n#transformers #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ 22, 6, 4, 75, 23, 3, 5, 8, 9, 8, 34, 20, 4, 5, 5, 11, 13, 12, 3, 10, 6, 5, 6, 4, 5, 7, 49, 7, 7, 5, 5, 15, 7, 7, 8, 5 ]

[ "TAGS\n#transformers #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]

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Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) sqlcoder-7b-2 - bnb 8bits - Model creator: https://huggingface.co/defog/ - Original model: https://huggingface.co/defog/sqlcoder-7b-2/ Original model description: --- license: cc-by-sa-4.0 library_name: transformers pipeline_tag: text-generation --- # Update notice The model weights were updated at 7 AM UTC on Feb 7, 2024. The new model weights lead to a much more performant model – particularly for joins. If you downloaded the model before that, please redownload the weights for best performance. # Model Card for SQLCoder-7B-2 A capable large language model for natural language to SQL generation. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/603bbad3fd770a9997b57cb6/AYUE2y14vy2XkD9MZpScu.png) ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [Defog, Inc](https://defog.ai) - **Model type:** [Text to SQL] - **License:** [CC-by-SA-4.0] - **Finetuned from model:** [CodeLlama-7B] ### Model Sources [optional] - [**HuggingFace:**](https://huggingface.co/defog/sqlcoder-70b-alpha) - [**GitHub:**](https://github.com/defog-ai/sqlcoder) - [**Demo:**](https://defog.ai/sqlcoder-demo/) ## Uses This model is intended to be used by non-technical users to understand data inside their SQL databases. It is meant as an analytics tool, and not as a database admin tool. This model has not been trained to reject malicious requests from users with write access to databases, and should only be used by users with read-only access. ## How to Get Started with the Model Use the code [here](https://github.com/defog-ai/sqlcoder/blob/main/inference.py) to get started with the model. ## Prompt Please use the following prompt for optimal results. Please remember to use `do_sample=False` and `num_beams=4` for optimal results. ``` ### Task Generate a SQL query to answer [QUESTION]{user_question}[/QUESTION] ### Database Schema The query will run on a database with the following schema: {table_metadata_string_DDL_statements} ### Answer Given the database schema, here is the SQL query that [QUESTION]{user_question}[/QUESTION] [SQL] ``` ## Evaluation This model was evaluated on [SQL-Eval](https://github.com/defog-ai/sql-eval), a PostgreSQL based evaluation framework developed by Defog for testing and alignment of model capabilities. You can read more about the methodology behind SQLEval [here](https://defog.ai/blog/open-sourcing-sqleval/). ### Results We classified each generated question into one of 6 categories. The table displays the percentage of questions answered correctly by each model, broken down by category. | | date | group_by | order_by | ratio | join | where | | -------------- | ---- | -------- | -------- | ----- | ---- | ----- | | sqlcoder-70b | 96 | 91.4 | 97.1 | 85.7 | 97.1 | 91.4 | | sqlcoder-7b-2 | 96 | 91.4 | 94.3 | 91.4 | 94.3 | 77.1 | | sqlcoder-34b | 80 | 94.3 | 85.7 | 77.1 | 85.7 | 80 | | gpt-4 | 72 | 94.3 | 97.1 | 80 | 91.4 | 80 | | gpt-4-turbo | 76 | 91.4 | 91.4 | 62.8 | 88.6 | 77.1 | | natural-sql-7b | 56 | 88.6 | 85.7 | 60 | 88.6 | 80 | | sqlcoder-7b | 64 | 82.9 | 74.3 | 54.3 | 74.3 | 74.3 | | gpt-3.5 | 72 | 77.1 | 82.8 | 34.3 | 65.7 | 71.4 | | claude-2 | 52 | 71.4 | 74.3 | 57.1 | 65.7 | 62.9 | ## Model Card Contact Contact us on X at [@defogdata](https://twitter.com/defogdata), or on email at [founders@defog.ai](mailto:founders@defog.ai)

{}

RichardErkhov/defog_-_sqlcoder-7b-2-8bits

null

[ "safetensors", "region:us" ]

null

2024-05-03T19:13:29+00:00

[]

TAGS #safetensors #region-us

Quantization made by Richard Erkhov. Github Discord Request more models sqlcoder-7b-2 - bnb 8bits * Model creator: URL * Original model: URL Original model description: --------------------------- license: cc-by-sa-4.0 library\_name: transformers pipeline\_tag: text-generation -------------------------------------------------------------------------------- Update notice ============= The model weights were updated at 7 AM UTC on Feb 7, 2024. The new model weights lead to a much more performant model – particularly for joins. If you downloaded the model before that, please redownload the weights for best performance. Model Card for SQLCoder-7B-2 ============================ A capable large language model for natural language to SQL generation. !image/png Model Details ------------- ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. * Developed by: Defog, Inc * Model type: [Text to SQL] * License: [CC-by-SA-4.0] * Finetuned from model: [CodeLlama-7B] ### Model Sources [optional] * HuggingFace: * GitHub: * Demo: Uses ---- This model is intended to be used by non-technical users to understand data inside their SQL databases. It is meant as an analytics tool, and not as a database admin tool. This model has not been trained to reject malicious requests from users with write access to databases, and should only be used by users with read-only access. How to Get Started with the Model --------------------------------- Use the code here to get started with the model. Prompt ------ Please use the following prompt for optimal results. Please remember to use 'do\_sample=False' and 'num\_beams=4' for optimal results. Evaluation ---------- This model was evaluated on SQL-Eval, a PostgreSQL based evaluation framework developed by Defog for testing and alignment of model capabilities. You can read more about the methodology behind SQLEval here. ### Results We classified each generated question into one of 6 categories. The table displays the percentage of questions answered correctly by each model, broken down by category. Model Card Contact ------------------ Contact us on X at @defogdata, or on email at founders@URL

[ "### Model Description\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n\n* Developed by: Defog, Inc\n* Model type: [Text to SQL]\n* License: [CC-by-SA-4.0]\n* Finetuned from model: [CodeLlama-7B]", "### Model Sources [optional]\n\n\n* HuggingFace:\n* GitHub:\n* Demo:\n\n\nUses\n----\n\n\nThis model is intended to be used by non-technical users to understand data inside their SQL databases. It is meant as an analytics tool, and not as a database admin tool.\n\n\nThis model has not been trained to reject malicious requests from users with write access to databases, and should only be used by users with read-only access.\n\n\nHow to Get Started with the Model\n---------------------------------\n\n\nUse the code here to get started with the model.\n\n\nPrompt\n------\n\n\nPlease use the following prompt for optimal results. Please remember to use 'do\\_sample=False' and 'num\\_beams=4' for optimal results.\n\n\nEvaluation\n----------\n\n\nThis model was evaluated on SQL-Eval, a PostgreSQL based evaluation framework developed by Defog for testing and alignment of model capabilities.\n\n\nYou can read more about the methodology behind SQLEval here.", "### Results\n\n\nWe classified each generated question into one of 6 categories. The table displays the percentage of questions answered correctly by each model, broken down by category.\n\n\n\nModel Card Contact\n------------------\n\n\nContact us on X at @defogdata, or on email at founders@URL" ]

[ "TAGS\n#safetensors #region-us \n", "### Model Description\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n\n* Developed by: Defog, Inc\n* Model type: [Text to SQL]\n* License: [CC-by-SA-4.0]\n* Finetuned from model: [CodeLlama-7B]", "### Model Sources [optional]\n\n\n* HuggingFace:\n* GitHub:\n* Demo:\n\n\nUses\n----\n\n\nThis model is intended to be used by non-technical users to understand data inside their SQL databases. It is meant as an analytics tool, and not as a database admin tool.\n\n\nThis model has not been trained to reject malicious requests from users with write access to databases, and should only be used by users with read-only access.\n\n\nHow to Get Started with the Model\n---------------------------------\n\n\nUse the code here to get started with the model.\n\n\nPrompt\n------\n\n\nPlease use the following prompt for optimal results. Please remember to use 'do\\_sample=False' and 'num\\_beams=4' for optimal results.\n\n\nEvaluation\n----------\n\n\nThis model was evaluated on SQL-Eval, a PostgreSQL based evaluation framework developed by Defog for testing and alignment of model capabilities.\n\n\nYou can read more about the methodology behind SQLEval here.", "### Results\n\n\nWe classified each generated question into one of 6 categories. The table displays the percentage of questions answered correctly by each model, broken down by category.\n\n\n\nModel Card Contact\n------------------\n\n\nContact us on X at @defogdata, or on email at founders@URL" ]

[ 9, 76, 241, 73 ]

[ "TAGS\n#safetensors #region-us \n### Model Description\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n\n* Developed by: Defog, Inc\n* Model type: [Text to SQL]\n* License: [CC-by-SA-4.0]\n* Finetuned from model: [CodeLlama-7B]### Model Sources [optional]\n\n\n* HuggingFace:\n* GitHub:\n* Demo:\n\n\nUses\n----\n\n\nThis model is intended to be used by non-technical users to understand data inside their SQL databases. It is meant as an analytics tool, and not as a database admin tool.\n\n\nThis model has not been trained to reject malicious requests from users with write access to databases, and should only be used by users with read-only access.\n\n\nHow to Get Started with the Model\n---------------------------------\n\n\nUse the code here to get started with the model.\n\n\nPrompt\n------\n\n\nPlease use the following prompt for optimal results. Please remember to use 'do\\_sample=False' and 'num\\_beams=4' for optimal results.\n\n\nEvaluation\n----------\n\n\nThis model was evaluated on SQL-Eval, a PostgreSQL based evaluation framework developed by Defog for testing and alignment of model capabilities.\n\n\nYou can read more about the methodology behind SQLEval here.### Results\n\n\nWe classified each generated question into one of 6 categories. The table displays the percentage of questions answered correctly by each model, broken down by category.\n\n\n\nModel Card Contact\n------------------\n\n\nContact us on X at @defogdata, or on email at founders@URL" ]

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# GUE_tf_2-seqsight_4096_512_15M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_tf_2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_2) dataset. It achieves the following results on the evaluation set: - Loss: 0.4762 - F1 Score: 0.7889 - Accuracy: 0.789 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.5955 | 1.34 | 200 | 0.5400 | 0.7222 | 0.723 | | 0.5387 | 2.68 | 400 | 0.5279 | 0.7378 | 0.738 | | 0.5259 | 4.03 | 600 | 0.5212 | 0.7440 | 0.744 | | 0.5216 | 5.37 | 800 | 0.5194 | 0.7418 | 0.742 | | 0.5186 | 6.71 | 1000 | 0.5178 | 0.7380 | 0.738 | | 0.5121 | 8.05 | 1200 | 0.5113 | 0.7370 | 0.737 | | 0.5072 | 9.4 | 1400 | 0.5088 | 0.7378 | 0.738 | | 0.5049 | 10.74 | 1600 | 0.5100 | 0.7390 | 0.739 | | 0.5029 | 12.08 | 1800 | 0.5164 | 0.7475 | 0.748 | | 0.4997 | 13.42 | 2000 | 0.5137 | 0.7435 | 0.744 | | 0.5004 | 14.77 | 2200 | 0.5058 | 0.7422 | 0.743 | | 0.4932 | 16.11 | 2400 | 0.5088 | 0.7445 | 0.745 | | 0.4954 | 17.45 | 2600 | 0.5046 | 0.7419 | 0.742 | | 0.489 | 18.79 | 2800 | 0.4987 | 0.7417 | 0.742 | | 0.4875 | 20.13 | 3000 | 0.5027 | 0.7400 | 0.74 | | 0.486 | 21.48 | 3200 | 0.5136 | 0.7389 | 0.74 | | 0.4861 | 22.82 | 3400 | 0.5056 | 0.7339 | 0.734 | | 0.4817 | 24.16 | 3600 | 0.4967 | 0.7400 | 0.74 | | 0.4779 | 25.5 | 3800 | 0.4973 | 0.7370 | 0.737 | | 0.4792 | 26.85 | 4000 | 0.5002 | 0.7398 | 0.74 | | 0.4759 | 28.19 | 4200 | 0.5024 | 0.7369 | 0.737 | | 0.4746 | 29.53 | 4400 | 0.5073 | 0.7470 | 0.747 | | 0.4749 | 30.87 | 4600 | 0.5034 | 0.7409 | 0.741 | | 0.4733 | 32.21 | 4800 | 0.4998 | 0.7419 | 0.742 | | 0.4726 | 33.56 | 5000 | 0.5061 | 0.7393 | 0.74 | | 0.4737 | 34.9 | 5200 | 0.5063 | 0.7414 | 0.742 | | 0.4669 | 36.24 | 5400 | 0.4962 | 0.7449 | 0.745 | | 0.469 | 37.58 | 5600 | 0.5000 | 0.7450 | 0.745 | | 0.4658 | 38.93 | 5800 | 0.5001 | 0.7380 | 0.738 | | 0.4631 | 40.27 | 6000 | 0.5003 | 0.7379 | 0.738 | | 0.464 | 41.61 | 6200 | 0.4970 | 0.7400 | 0.74 | | 0.4623 | 42.95 | 6400 | 0.5046 | 0.7459 | 0.746 | | 0.46 | 44.3 | 6600 | 0.5083 | 0.7489 | 0.749 | | 0.4634 | 45.64 | 6800 | 0.5060 | 0.7437 | 0.744 | | 0.4588 | 46.98 | 7000 | 0.5045 | 0.7439 | 0.744 | | 0.4597 | 48.32 | 7200 | 0.5028 | 0.746 | 0.746 | | 0.4557 | 49.66 | 7400 | 0.5030 | 0.7510 | 0.751 | | 0.4585 | 51.01 | 7600 | 0.5068 | 0.7386 | 0.739 | | 0.4579 | 52.35 | 7800 | 0.5012 | 0.7440 | 0.744 | | 0.4594 | 53.69 | 8000 | 0.5003 | 0.7460 | 0.746 | | 0.4561 | 55.03 | 8200 | 0.5002 | 0.7450 | 0.745 | | 0.4584 | 56.38 | 8400 | 0.5024 | 0.7428 | 0.743 | | 0.4565 | 57.72 | 8600 | 0.5004 | 0.7470 | 0.747 | | 0.4528 | 59.06 | 8800 | 0.5026 | 0.7459 | 0.746 | | 0.4547 | 60.4 | 9000 | 0.5034 | 0.7458 | 0.746 | | 0.4547 | 61.74 | 9200 | 0.5012 | 0.7459 | 0.746 | | 0.4584 | 63.09 | 9400 | 0.5009 | 0.7459 | 0.746 | | 0.4507 | 64.43 | 9600 | 0.5012 | 0.7489 | 0.749 | | 0.4539 | 65.77 | 9800 | 0.5020 | 0.7469 | 0.747 | | 0.4504 | 67.11 | 10000 | 0.5006 | 0.7470 | 0.747 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_tf_2-seqsight_4096_512_15M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_tf_2-seqsight_4096_512_15M-L8_f

null

[ "region:us" ]

null

2024-05-03T19:14:29+00:00

[]

TAGS #region-us

GUE\_tf\_2-seqsight\_4096\_512\_15M-L8\_f ========================================= This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_tf\_2 dataset. It achieves the following results on the evaluation set: * Loss: 0.4762 * F1 Score: 0.7889 * Accuracy: 0.789 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ "TAGS\n#region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ 5, 100, 5, 52 ]

[ "TAGS\n#region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000### Training results### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

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# GUE_tf_2-seqsight_4096_512_15M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_tf_2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_2) dataset. It achieves the following results on the evaluation set: - Loss: 0.4798 - F1 Score: 0.7869 - Accuracy: 0.787 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.578 | 1.34 | 200 | 0.5342 | 0.7303 | 0.733 | | 0.531 | 2.68 | 400 | 0.5274 | 0.7439 | 0.745 | | 0.5177 | 4.03 | 600 | 0.5157 | 0.7400 | 0.74 | | 0.5099 | 5.37 | 800 | 0.5128 | 0.7489 | 0.749 | | 0.5048 | 6.71 | 1000 | 0.5149 | 0.7448 | 0.745 | | 0.4968 | 8.05 | 1200 | 0.5041 | 0.7375 | 0.738 | | 0.4897 | 9.4 | 1400 | 0.5042 | 0.7520 | 0.752 | | 0.486 | 10.74 | 1600 | 0.5024 | 0.7480 | 0.748 | | 0.4817 | 12.08 | 1800 | 0.5059 | 0.7574 | 0.758 | | 0.4755 | 13.42 | 2000 | 0.5121 | 0.7437 | 0.744 | | 0.4763 | 14.77 | 2200 | 0.5078 | 0.7339 | 0.736 | | 0.4663 | 16.11 | 2400 | 0.5129 | 0.7577 | 0.758 | | 0.4685 | 17.45 | 2600 | 0.5037 | 0.7478 | 0.748 | | 0.4603 | 18.79 | 2800 | 0.4975 | 0.7444 | 0.745 | | 0.4557 | 20.13 | 3000 | 0.5109 | 0.7469 | 0.747 | | 0.4502 | 21.48 | 3200 | 0.5222 | 0.7300 | 0.731 | | 0.4525 | 22.82 | 3400 | 0.5181 | 0.7539 | 0.754 | | 0.4457 | 24.16 | 3600 | 0.5046 | 0.7480 | 0.748 | | 0.4382 | 25.5 | 3800 | 0.5103 | 0.7479 | 0.748 | | 0.4378 | 26.85 | 4000 | 0.5076 | 0.7479 | 0.748 | | 0.4323 | 28.19 | 4200 | 0.5127 | 0.7404 | 0.741 | | 0.4281 | 29.53 | 4400 | 0.5187 | 0.7369 | 0.737 | | 0.4288 | 30.87 | 4600 | 0.5104 | 0.7460 | 0.746 | | 0.4232 | 32.21 | 4800 | 0.5187 | 0.7560 | 0.756 | | 0.4203 | 33.56 | 5000 | 0.5202 | 0.7537 | 0.754 | | 0.4205 | 34.9 | 5200 | 0.5271 | 0.7454 | 0.746 | | 0.409 | 36.24 | 5400 | 0.5216 | 0.7489 | 0.749 | | 0.4114 | 37.58 | 5600 | 0.5241 | 0.7477 | 0.748 | | 0.4077 | 38.93 | 5800 | 0.5173 | 0.7479 | 0.748 | | 0.404 | 40.27 | 6000 | 0.5202 | 0.7560 | 0.756 | | 0.4026 | 41.61 | 6200 | 0.5207 | 0.7430 | 0.743 | | 0.3983 | 42.95 | 6400 | 0.5391 | 0.7477 | 0.748 | | 0.3954 | 44.3 | 6600 | 0.5431 | 0.7377 | 0.738 | | 0.3973 | 45.64 | 6800 | 0.5416 | 0.7351 | 0.736 | | 0.3911 | 46.98 | 7000 | 0.5404 | 0.7419 | 0.742 | | 0.3916 | 48.32 | 7200 | 0.5340 | 0.7429 | 0.743 | | 0.3874 | 49.66 | 7400 | 0.5330 | 0.7450 | 0.745 | | 0.3831 | 51.01 | 7600 | 0.5419 | 0.7387 | 0.739 | | 0.3811 | 52.35 | 7800 | 0.5460 | 0.7430 | 0.743 | | 0.3823 | 53.69 | 8000 | 0.5400 | 0.7440 | 0.744 | | 0.3795 | 55.03 | 8200 | 0.5479 | 0.7407 | 0.741 | | 0.3828 | 56.38 | 8400 | 0.5518 | 0.7407 | 0.741 | | 0.379 | 57.72 | 8600 | 0.5405 | 0.7458 | 0.746 | | 0.3751 | 59.06 | 8800 | 0.5438 | 0.7388 | 0.739 | | 0.3759 | 60.4 | 9000 | 0.5491 | 0.7407 | 0.741 | | 0.3729 | 61.74 | 9200 | 0.5489 | 0.7458 | 0.746 | | 0.3759 | 63.09 | 9400 | 0.5501 | 0.7437 | 0.744 | | 0.3732 | 64.43 | 9600 | 0.5483 | 0.7388 | 0.739 | | 0.375 | 65.77 | 9800 | 0.5503 | 0.7446 | 0.745 | | 0.369 | 67.11 | 10000 | 0.5488 | 0.7369 | 0.737 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_tf_2-seqsight_4096_512_15M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_tf_2-seqsight_4096_512_15M-L32_f

null

[ "region:us" ]

null

2024-05-03T19:15:29+00:00

[]

TAGS #region-us

GUE\_tf\_2-seqsight\_4096\_512\_15M-L32\_f ========================================== This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_tf\_2 dataset. It achieves the following results on the evaluation set: * Loss: 0.4798 * F1 Score: 0.7869 * Accuracy: 0.787 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ "TAGS\n#region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ 5, 100, 5, 52 ]

[ "TAGS\n#region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000### Training results### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

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# GUE_virus_covid-seqsight_4096_512_15M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_virus_covid](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_virus_covid) dataset. It achieves the following results on the evaluation set: - Loss: 1.9282 - F1 Score: 0.2779 - Accuracy: 0.2832 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 2.1857 | 0.35 | 200 | 2.1860 | 0.0716 | 0.1278 | | 2.183 | 0.7 | 400 | 2.1840 | 0.0572 | 0.1247 | | 2.1793 | 1.05 | 600 | 2.1784 | 0.0832 | 0.1352 | | 2.1765 | 1.4 | 800 | 2.1736 | 0.0908 | 0.1396 | | 2.1694 | 1.75 | 1000 | 2.1685 | 0.1077 | 0.1492 | | 2.1674 | 2.09 | 1200 | 2.1739 | 0.0989 | 0.1451 | | 2.1644 | 2.44 | 1400 | 2.1706 | 0.1217 | 0.1480 | | 2.1612 | 2.79 | 1600 | 2.1591 | 0.1324 | 0.1615 | | 2.1568 | 3.14 | 1800 | 2.1538 | 0.1270 | 0.1687 | | 2.1522 | 3.49 | 2000 | 2.1578 | 0.1333 | 0.1689 | | 2.1535 | 3.84 | 2200 | 2.1464 | 0.1515 | 0.1814 | | 2.1446 | 4.19 | 2400 | 2.1428 | 0.1506 | 0.1716 | | 2.1418 | 4.54 | 2600 | 2.1369 | 0.1560 | 0.1859 | | 2.138 | 4.89 | 2800 | 2.1304 | 0.1727 | 0.1887 | | 2.133 | 5.24 | 3000 | 2.1352 | 0.1610 | 0.1908 | | 2.1303 | 5.58 | 3200 | 2.1227 | 0.1787 | 0.2040 | | 2.127 | 5.93 | 3400 | 2.1300 | 0.1451 | 0.1809 | | 2.121 | 6.28 | 3600 | 2.1118 | 0.1827 | 0.2046 | | 2.1132 | 6.63 | 3800 | 2.0989 | 0.1781 | 0.2027 | | 2.11 | 6.98 | 4000 | 2.0828 | 0.2078 | 0.2254 | | 2.0955 | 7.33 | 4200 | 2.0556 | 0.2196 | 0.2338 | | 2.0834 | 7.68 | 4400 | 2.0488 | 0.2224 | 0.2342 | | 2.0747 | 8.03 | 4600 | 2.0685 | 0.1803 | 0.2083 | | 2.0662 | 8.38 | 4800 | 2.0344 | 0.2150 | 0.2323 | | 2.0627 | 8.73 | 5000 | 2.0267 | 0.2107 | 0.2333 | | 2.0541 | 9.08 | 5200 | 2.0213 | 0.2244 | 0.2355 | | 2.0482 | 9.42 | 5400 | 2.0056 | 0.2347 | 0.2490 | | 2.0413 | 9.77 | 5600 | 2.0041 | 0.2293 | 0.2441 | | 2.0395 | 10.12 | 5800 | 1.9909 | 0.2505 | 0.2573 | | 2.0322 | 10.47 | 6000 | 1.9841 | 0.2563 | 0.2616 | | 2.0275 | 10.82 | 6200 | 1.9875 | 0.2414 | 0.2515 | | 2.0227 | 11.17 | 6400 | 1.9840 | 0.2401 | 0.2509 | | 2.0205 | 11.52 | 6600 | 1.9861 | 0.2374 | 0.2514 | | 2.0191 | 11.87 | 6800 | 1.9717 | 0.2484 | 0.2594 | | 2.0118 | 12.22 | 7000 | 1.9615 | 0.2657 | 0.2700 | | 2.008 | 12.57 | 7200 | 1.9528 | 0.2658 | 0.2708 | | 2.0108 | 12.91 | 7400 | 1.9626 | 0.2555 | 0.2638 | | 2.0043 | 13.26 | 7600 | 1.9508 | 0.2567 | 0.2681 | | 1.9972 | 13.61 | 7800 | 1.9566 | 0.2538 | 0.2635 | | 1.9999 | 13.96 | 8000 | 1.9473 | 0.2719 | 0.2755 | | 1.9947 | 14.31 | 8200 | 1.9432 | 0.2678 | 0.2758 | | 1.9987 | 14.66 | 8400 | 1.9337 | 0.2747 | 0.2785 | | 1.9902 | 15.01 | 8600 | 1.9422 | 0.2650 | 0.2717 | | 1.9921 | 15.36 | 8800 | 1.9332 | 0.2762 | 0.2783 | | 1.9841 | 15.71 | 9000 | 1.9405 | 0.2699 | 0.2780 | | 1.9876 | 16.06 | 9200 | 1.9298 | 0.2772 | 0.2806 | | 1.9878 | 16.4 | 9400 | 1.9299 | 0.2749 | 0.2798 | | 1.9869 | 16.75 | 9600 | 1.9348 | 0.2755 | 0.2804 | | 1.9865 | 17.1 | 9800 | 1.9314 | 0.2739 | 0.2793 | | 1.9921 | 17.45 | 10000 | 1.9304 | 0.2764 | 0.2804 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_virus_covid-seqsight_4096_512_15M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_virus_covid-seqsight_4096_512_15M-L1_f

null

[ "region:us" ]

null

2024-05-03T19:16:42+00:00

[]

TAGS #region-us

GUE\_virus\_covid-seqsight\_4096\_512\_15M-L1\_f ================================================ This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_virus\_covid dataset. It achieves the following results on the evaluation set: * Loss: 1.9282 * F1 Score: 0.2779 * Accuracy: 0.2832 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ "TAGS\n#region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ 5, 100, 5, 52 ]

[ "TAGS\n#region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000### Training results### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

null

# Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

{"library_name": "transformers", "tags": []}

golf2248/ofn2ele

null

[ "region:us" ]

null

2024-05-03T19:16:49+00:00

[]

TAGS #region-us

# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "TAGS\n#region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

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[ "TAGS\n#region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]

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# GUE_virus_covid-seqsight_4096_512_15M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_virus_covid](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_virus_covid) dataset. It achieves the following results on the evaluation set: - Loss: 1.5376 - F1 Score: 0.4255 - Accuracy: 0.4204 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 2.1853 | 0.35 | 200 | 2.1837 | 0.0874 | 0.1342 | | 2.1805 | 0.7 | 400 | 2.1771 | 0.0925 | 0.1295 | | 2.1715 | 1.05 | 600 | 2.1691 | 0.1098 | 0.1369 | | 2.1617 | 1.4 | 800 | 2.1614 | 0.1110 | 0.1502 | | 2.1494 | 1.75 | 1000 | 2.1453 | 0.1504 | 0.1804 | | 2.1399 | 2.09 | 1200 | 2.1424 | 0.1226 | 0.1747 | | 2.11 | 2.44 | 1400 | 2.0699 | 0.1890 | 0.2135 | | 2.0694 | 2.79 | 1600 | 2.0231 | 0.2130 | 0.2406 | | 2.0288 | 3.14 | 1800 | 2.0134 | 0.2062 | 0.2318 | | 1.9962 | 3.49 | 2000 | 1.9502 | 0.2475 | 0.2598 | | 1.9712 | 3.84 | 2200 | 1.8961 | 0.2710 | 0.2816 | | 1.9382 | 4.19 | 2400 | 1.8577 | 0.2936 | 0.2901 | | 1.9121 | 4.54 | 2600 | 1.8328 | 0.3132 | 0.3178 | | 1.8976 | 4.89 | 2800 | 1.8175 | 0.3134 | 0.3129 | | 1.875 | 5.24 | 3000 | 1.7826 | 0.3280 | 0.3340 | | 1.8617 | 5.58 | 3200 | 1.7518 | 0.3499 | 0.3488 | | 1.8365 | 5.93 | 3400 | 1.7553 | 0.3296 | 0.3388 | | 1.8209 | 6.28 | 3600 | 1.7260 | 0.3515 | 0.3516 | | 1.8059 | 6.63 | 3800 | 1.7081 | 0.3620 | 0.3599 | | 1.8003 | 6.98 | 4000 | 1.7012 | 0.3732 | 0.3702 | | 1.7834 | 7.33 | 4200 | 1.6943 | 0.3664 | 0.3658 | | 1.7706 | 7.68 | 4400 | 1.6790 | 0.3783 | 0.3660 | | 1.767 | 8.03 | 4600 | 1.6793 | 0.3684 | 0.3688 | | 1.7547 | 8.38 | 4800 | 1.6680 | 0.3748 | 0.3752 | | 1.7509 | 8.73 | 5000 | 1.6592 | 0.3763 | 0.3802 | | 1.7496 | 9.08 | 5200 | 1.6561 | 0.3869 | 0.3803 | | 1.7273 | 9.42 | 5400 | 1.6421 | 0.3869 | 0.3880 | | 1.7283 | 9.77 | 5600 | 1.6331 | 0.3979 | 0.3955 | | 1.725 | 10.12 | 5800 | 1.6186 | 0.4024 | 0.3932 | | 1.7221 | 10.47 | 6000 | 1.6145 | 0.3986 | 0.3946 | | 1.7101 | 10.82 | 6200 | 1.6078 | 0.4082 | 0.4012 | | 1.6922 | 11.17 | 6400 | 1.6023 | 0.4073 | 0.4024 | | 1.6973 | 11.52 | 6600 | 1.5917 | 0.4116 | 0.4045 | | 1.6989 | 11.87 | 6800 | 1.5862 | 0.4106 | 0.4053 | | 1.684 | 12.22 | 7000 | 1.5780 | 0.4176 | 0.4108 | | 1.674 | 12.57 | 7200 | 1.5750 | 0.4172 | 0.4123 | | 1.6799 | 12.91 | 7400 | 1.5693 | 0.4194 | 0.4140 | | 1.6687 | 13.26 | 7600 | 1.5574 | 0.4183 | 0.4153 | | 1.6716 | 13.61 | 7800 | 1.5663 | 0.4222 | 0.4162 | | 1.6615 | 13.96 | 8000 | 1.5567 | 0.4226 | 0.4177 | | 1.6562 | 14.31 | 8200 | 1.5533 | 0.4217 | 0.4166 | | 1.6584 | 14.66 | 8400 | 1.5481 | 0.4290 | 0.4196 | | 1.656 | 15.01 | 8600 | 1.5455 | 0.4272 | 0.4237 | | 1.6563 | 15.36 | 8800 | 1.5480 | 0.4297 | 0.4204 | | 1.639 | 15.71 | 9000 | 1.5463 | 0.4260 | 0.4224 | | 1.6507 | 16.06 | 9200 | 1.5438 | 0.4242 | 0.4192 | | 1.6477 | 16.4 | 9400 | 1.5385 | 0.4275 | 0.4226 | | 1.6475 | 16.75 | 9600 | 1.5404 | 0.4289 | 0.4243 | | 1.6414 | 17.1 | 9800 | 1.5406 | 0.4294 | 0.4249 | | 1.6511 | 17.45 | 10000 | 1.5388 | 0.4300 | 0.4249 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_virus_covid-seqsight_4096_512_15M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_virus_covid-seqsight_4096_512_15M-L8_f

null

[ "region:us" ]

null

2024-05-03T19:16:59+00:00

[]

TAGS #region-us

GUE\_virus\_covid-seqsight\_4096\_512\_15M-L8\_f ================================================ This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_4096\_512\_15M on the mahdibaghbanzadeh/GUE\_virus\_covid dataset. It achieves the following results on the evaluation set: * Loss: 1.5376 * F1 Score: 0.4255 * Accuracy: 0.4204 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 10000 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.1 * Tokenizers 0.15.2

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ "TAGS\n#region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]

[ 5, 100, 5, 52 ]

[ "TAGS\n#region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000### Training results### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2" ]