e5-args-1 / README.md

Add new SentenceTransformer model.

4e9816d verified 3 months ago

15.5 kB

	---
	base_model: intfloat/multilingual-e5-base
	datasets: []
	language: []
	library_name: sentence-transformers
	pipeline_tag: sentence-similarity
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:100
	- loss:TripletLoss
	widget:
	- source_sentence: How many athletes from region 151 have won a medal?
	sentences:
	- athletes refer to person_id; region 151 refers to region_id = 151; won a medal
	refers to medal_id <> 4;
	- Rio de Janeiro refers to city_name = 'Rio de Janeiro';
	- the highest number of participants refers to MAX(COUNT(person_id)); the lowest
	number of participants refers to MIN(COUNT(person_id)); Which summer Olympic refers
	to games_name where season = 'Summer';
	- source_sentence: What is the id of Rio de Janeiro?
	sentences:
	- year refers to games_year;
	- athletes refer to person_id; region 151 refers to region_id = 151; won a medal
	refers to medal_id <> 4;
	- Rio de Janeiro refers to city_name = 'Rio de Janeiro';
	- source_sentence: Please list the Asian populations of all the residential areas
	with the bad alias "URB San Joaquin".
	sentences:
	- '"URB San Joaquin" is the bad_alias'
	- name of congressman implies full name which refers to first_name, last_name; Guanica
	is the city;
	- '"URB San Joaquin" is the bad_alias'
	- source_sentence: State the male population for all zip code which were under the
	Berlin, NH CBSA.
	sentences:
	- '"Berlin, NH" is the CBSA_name'
	- '"Barre, VT" is the CBSA_name'
	- representative's full names refer to first_name, last_name; area which has highest
	population in 2020 refers to MAX(population_2020);
	- source_sentence: Which state has the most bad aliases?
	sentences:
	- '"York" is the city; ''ME'' is the state; type refers to CBSA_type'
	- the most bad aliases refer to MAX(COUNT(bad_alias));
	- precise location refers to latitude, longitude
	---

	# SentenceTransformer based on intfloat/multilingual-e5-base

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) on the train and test datasets. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

	## Model Details

	### Model Description
	- Model Type: Sentence Transformer
	- Base model: [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) <!-- at revision d13f1b27baf31030b7fd040960d60d909913633f -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 768 tokens
	- Similarity Function: Cosine Similarity
	- Training Datasets:
	- train
	- test
	<!-- - Language: Unknown -->
	<!-- - License: Unknown -->

	### Model Sources

	- Documentation: [Sentence Transformers Documentation](https://sbert.net)
	- Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
	- Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

	### Full Model Architecture

	```
	SentenceTransformer(
	(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
	(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
	(2): Normalize()
	)
	```

	## Usage

	### Direct Usage (Sentence Transformers)

	First install the Sentence Transformers library:

	```bash
	pip install -U sentence-transformers
	```

	Then you can load this model and run inference.
	```python
	from sentence_transformers import SentenceTransformer

	# Download from the 🤗 Hub
	model = SentenceTransformer("DariaaaS/e5-args-1")
	# Run inference
	sentences = [
	'Which state has the most bad aliases?',
	'the most bad aliases refer to MAX(COUNT(bad_alias));',
	'precise location refers to latitude, longitude',
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 768]

	# Get the similarity scores for the embeddings
	similarities = model.similarity(embeddings, embeddings)
	print(similarities.shape)
	# [3, 3]
	```

	<!--
	### Direct Usage (Transformers)

	<details><summary>Click to see the direct usage in Transformers</summary>

	</details>
	-->

	<!--
	### Downstream Usage (Sentence Transformers)

	You can finetune this model on your own dataset.

	<details><summary>Click to expand</summary>

	</details>
	-->

	<!--
	### Out-of-Scope Use

	List how the model may foreseeably be misused and address what users ought not to do with the model.
	-->

	<!--
	## Bias, Risks and Limitations

	What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.
	-->

	<!--
	### Recommendations

	What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.
	-->

	## Training Details

	### Training Datasets

	#### train

	* Dataset: train
	* Size: 80 training samples
	* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| query \| positive \| negative \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|
	\| type \| string \| string \| string \|
	\| details \| <ul><li>min: 11 tokens</li><li>mean: 19.75 tokens</li><li>max: 31 tokens</li></ul> \| <ul><li>min: 8 tokens</li><li>mean: 18.12 tokens</li><li>max: 33 tokens</li></ul> \| <ul><li>min: 8 tokens</li><li>mean: 28.56 tokens</li><li>max: 54 tokens</li></ul> \|
	* Samples:
	\| query \| positive \| negative \|
	\|:-----------------------------------------------------\|:------------------------------------------\|:--------------------------------------------------------------------------------------------------------\|
	\| <code>How many zip codes are under Barre, VT?</code> \| <code>"Barre, VT" is the CBSA_name</code> \| <code>coordinates refers to latitude, longitude; latitude = '18.090875; longitude = '-66.867756'</code> \|
	\| <code>How many zip codes are under Barre, VT?</code> \| <code>"Barre, VT" is the CBSA_name</code> \| <code>name of county refers to county</code> \|
	\| <code>How many zip codes are under Barre, VT?</code> \| <code>"Barre, VT" is the CBSA_name</code> \| <code>median age over 40 refers to median_age > 40</code> \|
	* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
	```json
	{
	"distance_metric": "TripletDistanceMetric.EUCLIDEAN",
	"triplet_margin": 5
	}
	```

	#### test

	* Dataset: test
	* Size: 20 training samples
	* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| query \| positive \| negative \|
	\|:--------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|
	\| type \| string \| string \| string \|
	\| details \| <ul><li>min: 11 tokens</li><li>mean: 12.5 tokens</li><li>max: 14 tokens</li></ul> \| <ul><li>min: 14 tokens</li><li>mean: 22.5 tokens</li><li>max: 34 tokens</li></ul> \| <ul><li>min: 9 tokens</li><li>mean: 23.45 tokens</li><li>max: 56 tokens</li></ul> \|
	* Samples:
	\| query \| positive \| negative \|
	\|:---------------------------------------------------------\|:-------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------\|
	\| <code>Where is competitor Estelle Nze Minko from?</code> \| <code>Where competitor is from refers to region_name;</code> \| <code>NOC code refers to noc; the heaviest refers to MAX(weight);</code> \|
	\| <code>Where is competitor Estelle Nze Minko from?</code> \| <code>Where competitor is from refers to region_name;</code> \| <code>host city refers to city_name; the 1968 Winter Olympic Games refer to games_name = '1968 Winter';</code> \|
	\| <code>Where is competitor Estelle Nze Minko from?</code> \| <code>Where competitor is from refers to region_name;</code> \| <code>the gold medal refers to medal_name = 'Gold';</code> \|
	* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
	```json
	{
	"distance_metric": "TripletDistanceMetric.EUCLIDEAN",
	"triplet_margin": 5
	}
	```

	### Training Hyperparameters
	#### Non-Default Hyperparameters

	- `per_device_train_batch_size`: 16
	- `per_device_eval_batch_size`: 16
	- `learning_rate`: 2e-05
	- `num_train_epochs`: 4
	- `warmup_ratio`: 0.1
	- `fp16`: True

	#### All Hyperparameters
	<details><summary>Click to expand</summary>

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `eval_strategy`: no
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 16
	- `per_device_eval_batch_size`: 16
	- `per_gpu_train_batch_size`: None
	- `per_gpu_eval_batch_size`: None
	- `gradient_accumulation_steps`: 1
	- `eval_accumulation_steps`: None
	- `learning_rate`: 2e-05
	- `weight_decay`: 0.0
	- `adam_beta1`: 0.9
	- `adam_beta2`: 0.999
	- `adam_epsilon`: 1e-08
	- `max_grad_norm`: 1.0
	- `num_train_epochs`: 4
	- `max_steps`: -1
	- `lr_scheduler_type`: linear
	- `lr_scheduler_kwargs`: {}
	- `warmup_ratio`: 0.1
	- `warmup_steps`: 0
	- `log_level`: passive
	- `log_level_replica`: warning
	- `log_on_each_node`: True
	- `logging_nan_inf_filter`: True
	- `save_safetensors`: True
	- `save_on_each_node`: False
	- `save_only_model`: False
	- `restore_callback_states_from_checkpoint`: False
	- `no_cuda`: False
	- `use_cpu`: False
	- `use_mps_device`: False
	- `seed`: 42
	- `data_seed`: None
	- `jit_mode_eval`: False
	- `use_ipex`: False
	- `bf16`: False
	- `fp16`: True
	- `fp16_opt_level`: O1
	- `half_precision_backend`: auto
	- `bf16_full_eval`: False
	- `fp16_full_eval`: False
	- `tf32`: None
	- `local_rank`: 0
	- `ddp_backend`: None
	- `tpu_num_cores`: None
	- `tpu_metrics_debug`: False
	- `debug`: []
	- `dataloader_drop_last`: False
	- `dataloader_num_workers`: 0
	- `dataloader_prefetch_factor`: None
	- `past_index`: -1
	- `disable_tqdm`: False
	- `remove_unused_columns`: True
	- `label_names`: None
	- `load_best_model_at_end`: False
	- `ignore_data_skip`: False
	- `fsdp`: []
	- `fsdp_min_num_params`: 0
	- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
	- `fsdp_transformer_layer_cls_to_wrap`: None
	- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
	- `deepspeed`: None
	- `label_smoothing_factor`: 0.0
	- `optim`: adamw_torch
	- `optim_args`: None
	- `adafactor`: False
	- `group_by_length`: False
	- `length_column_name`: length
	- `ddp_find_unused_parameters`: None
	- `ddp_bucket_cap_mb`: None
	- `ddp_broadcast_buffers`: False
	- `dataloader_pin_memory`: True
	- `dataloader_persistent_workers`: False
	- `skip_memory_metrics`: True
	- `use_legacy_prediction_loop`: False
	- `push_to_hub`: False
	- `resume_from_checkpoint`: None
	- `hub_model_id`: None
	- `hub_strategy`: every_save
	- `hub_private_repo`: False
	- `hub_always_push`: False
	- `gradient_checkpointing`: False
	- `gradient_checkpointing_kwargs`: None
	- `include_inputs_for_metrics`: False
	- `eval_do_concat_batches`: True
	- `fp16_backend`: auto
	- `push_to_hub_model_id`: None
	- `push_to_hub_organization`: None
	- `mp_parameters`:
	- `auto_find_batch_size`: False
	- `full_determinism`: False
	- `torchdynamo`: None
	- `ray_scope`: last
	- `ddp_timeout`: 1800
	- `torch_compile`: False
	- `torch_compile_backend`: None
	- `torch_compile_mode`: None
	- `dispatch_batches`: None
	- `split_batches`: None
	- `include_tokens_per_second`: False
	- `include_num_input_tokens_seen`: False
	- `neftune_noise_alpha`: None
	- `optim_target_modules`: None
	- `batch_eval_metrics`: False
	- `eval_on_start`: False
	- `batch_sampler`: batch_sampler
	- `multi_dataset_batch_sampler`: proportional

	</details>

	### Framework Versions
	- Python: 3.10.12
	- Sentence Transformers: 3.0.1
	- Transformers: 4.42.4
	- PyTorch: 2.4.0+cu121
	- Accelerate: 0.32.1
	- Datasets: 2.21.0
	- Tokenizers: 0.19.1

	## Citation

	### BibTeX

	#### Sentence Transformers
	```bibtex
	@inproceedings{reimers-2019-sentence-bert,
	title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
	author = "Reimers, Nils and Gurevych, Iryna",
	booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
	month = "11",
	year = "2019",
	publisher = "Association for Computational Linguistics",
	url = "https://arxiv.org/abs/1908.10084",
	}
	```

	#### TripletLoss
	```bibtex
	@misc{hermans2017defense,
	title={In Defense of the Triplet Loss for Person Re-Identification},
	author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
	year={2017},
	eprint={1703.07737},
	archivePrefix={arXiv},
	primaryClass={cs.CV}
	}
	```

	<!--
	## Glossary

	Clearly define terms in order to be accessible across audiences.
	-->

	<!--
	## Model Card Authors

	Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.
	-->

	<!--
	## Model Card Contact

	Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.
	-->