|
--- |
|
base_model: Mihaiii/Venusaur |
|
datasets: |
|
- Mihaiii/qa-assistant-2 |
|
language: |
|
- en |
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library_name: sentence-transformers |
|
metrics: |
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- pearson_cosine |
|
- spearman_cosine |
|
- pearson_manhattan |
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- spearman_manhattan |
|
- pearson_euclidean |
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- spearman_euclidean |
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- pearson_dot |
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- spearman_dot |
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- pearson_max |
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- spearman_max |
|
pipeline_tag: sentence-similarity |
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tags: |
|
- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
|
- generated_from_trainer |
|
- dataset_size:16011 |
|
- loss:CosineSimilarityLoss |
|
widget: |
|
- source_sentence: What impact does high-speed rail have on connectivity between cities? |
|
sentences: |
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- Art supplies can be quite expensive, especially high-quality paints and brushes. |
|
- High-speed rail can be a more comfortable and convenient mode of travel compared |
|
to buses or cars. |
|
- Engineers use a variety of methods to test the safety of autonomous vehicles, |
|
including controlled track testing and public road trials. |
|
- source_sentence: What is the best soil type for growing tomatoes? |
|
sentences: |
|
- Sandy loam soil is often considered ideal for growing tomatoes due to its good |
|
drainage and nutrient-holding capacity. |
|
- Socialist political systems are often contrasted with capitalist systems, which |
|
prioritize private ownership and market-driven economies. |
|
- The core principles of Sikhism include the belief in one God, the importance of |
|
honest living, and the practice of selfless service. |
|
- source_sentence: What are the three main types of rocks? |
|
sentences: |
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- Mount Everest is the highest mountain in the world, located in the Himalayas. |
|
- Archaeologists sometimes face challenges such as funding and access to advanced |
|
technology, which can impact their ability to preserve findings. |
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- Some people are concerned about the ethical implications of genetic modification |
|
in food production. |
|
- source_sentence: How do vaccines help prevent diseases? |
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sentences: |
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- The theory also posits that during periods of economic downturn, increased government |
|
spending can help stimulate demand and pull the economy out of recession. |
|
- The Gurdwara is a place where Sikhs can participate in religious rituals and ceremonies, |
|
such as weddings and naming ceremonies. |
|
- The development of vaccines involves rigorous testing to ensure their safety and |
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efficacy before they are approved for public use. |
|
- source_sentence: What are the social structures of ants? |
|
sentences: |
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- The social hierarchy of ants is a complex system that ensures the survival and |
|
efficiency of the colony. |
|
- In a parliamentary system, the executive branch derives its legitimacy from and |
|
is accountable to the legislature; the executive and legislative branches are |
|
thus interconnected. |
|
- Proper waste management and recycling can contribute to a more sustainable farming |
|
operation. |
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model-index: |
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- name: SentenceTransformer based on Mihaiii/Venusaur |
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results: |
|
- task: |
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type: semantic-similarity |
|
name: Semantic Similarity |
|
dataset: |
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name: sts dev |
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type: sts-dev |
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metrics: |
|
- type: pearson_cosine |
|
value: 0.826101669872389 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.8277251878978443 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.8199515763304537 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.8225731321378551 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.8214525375708358 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.8236879484111633 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.8037304918463798 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.8082305683494836 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.826101669872389 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.8277251878978443 |
|
name: Spearman Max |
|
--- |
|
|
|
# SentenceTransformer based on Mihaiii/Venusaur |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Mihaiii/Venusaur](https://huggingface.co/Mihaiii/Venusaur) on the [Mihaiii/qa-assistant-2](https://huggingface.co/datasets/Mihaiii/qa-assistant-2) dataset. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
|
|
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## Model Details |
|
|
|
### Model Description |
|
- **Model Type:** Sentence Transformer |
|
- **Base model:** [Mihaiii/Venusaur](https://huggingface.co/Mihaiii/Venusaur) <!-- at revision 0dc817f0addbb7bab8feeeeaded538f9ffeb3419 --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 384 tokens |
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- **Similarity Function:** Cosine Similarity |
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- **Training Dataset:** |
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- [Mihaiii/qa-assistant-2](https://huggingface.co/datasets/Mihaiii/qa-assistant-2) |
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- **Language:** en |
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<!-- - **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( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel |
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(1): Pooling({'word_embedding_dimension': 384, '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}) |
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) |
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``` |
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|
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## 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 |
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from sentence_transformers import SentenceTransformer |
|
|
|
# Download from the 🤗 Hub |
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model = SentenceTransformer("sentence_transformers_model_id") |
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# Run inference |
|
sentences = [ |
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'What are the social structures of ants?', |
|
'The social hierarchy of ants is a complex system that ensures the survival and efficiency of the colony.', |
|
'In a parliamentary system, the executive branch derives its legitimacy from and is accountable to the legislature; the executive and legislative branches are thus interconnected.', |
|
] |
|
embeddings = model.encode(sentences) |
|
print(embeddings.shape) |
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# [3, 384] |
|
|
|
# Get the similarity scores for the embeddings |
|
similarities = model.similarity(embeddings, embeddings) |
|
print(similarities.shape) |
|
# [3, 3] |
|
``` |
|
|
|
<!-- |
|
### Direct Usage (Transformers) |
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|
|
<details><summary>Click to see the direct usage in Transformers</summary> |
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|
|
</details> |
|
--> |
|
|
|
<!-- |
|
### Downstream Usage (Sentence Transformers) |
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|
|
You can finetune this model on your own dataset. |
|
|
|
<details><summary>Click to expand</summary> |
|
|
|
</details> |
|
--> |
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|
|
<!-- |
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### Out-of-Scope Use |
|
|
|
*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
|
--> |
|
|
|
## Evaluation |
|
|
|
### Metrics |
|
|
|
#### Semantic Similarity |
|
* Dataset: `sts-dev` |
|
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| pearson_cosine | 0.8261 | |
|
| **spearman_cosine** | **0.8277** | |
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| pearson_manhattan | 0.82 | |
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| spearman_manhattan | 0.8226 | |
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| pearson_euclidean | 0.8215 | |
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| spearman_euclidean | 0.8237 | |
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| pearson_dot | 0.8037 | |
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| spearman_dot | 0.8082 | |
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| pearson_max | 0.8261 | |
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| spearman_max | 0.8277 | |
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|
|
<!-- |
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## Bias, Risks and Limitations |
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|
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
|
--> |
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|
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<!-- |
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### Recommendations |
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|
|
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
|
|
|
## Training Details |
|
|
|
### Training Dataset |
|
|
|
#### Mihaiii/qa-assistant-2 |
|
|
|
* Dataset: [Mihaiii/qa-assistant-2](https://huggingface.co/datasets/Mihaiii/qa-assistant-2) at [9650e69](https://huggingface.co/datasets/Mihaiii/qa-assistant-2/tree/9650e69ae0a030fa74a8706a20a168a613c43241) |
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* Size: 16,011 training samples |
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* Columns: <code>question</code>, <code>answer</code>, and <code>score</code> |
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* Approximate statistics based on the first 1000 samples: |
|
| | question | answer | score | |
|
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------| |
|
| type | string | string | float | |
|
| details | <ul><li>min: 6 tokens</li><li>mean: 12.73 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 22.42 tokens</li><li>max: 65 tokens</li></ul> | <ul><li>min: 0.02</li><li>mean: 0.53</li><li>max: 1.0</li></ul> | |
|
* Samples: |
|
| question | answer | score | |
|
|:-----------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------| |
|
| <code>Can you describe the process of robot path planning?</code> | <code>Robots can be programmed to perform a variety of tasks, from simple repetitive actions to complex decision-making processes.</code> | <code>0.27999999999999997</code> | |
|
| <code>Can humans live on Mars?</code> | <code>Mars is the fourth planet from the Sun and is often called the Red Planet due to its reddish appearance.</code> | <code>0.16</code> | |
|
| <code>What are the key elements of composition in abstract art?</code> | <code>The history of abstract art dates back to the early 20th century, with pioneers like Wassily Kandinsky and Piet Mondrian.</code> | <code>0.36</code> | |
|
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: |
|
```json |
|
{ |
|
"loss_fct": "torch.nn.modules.loss.MSELoss" |
|
} |
|
``` |
|
|
|
### Evaluation Dataset |
|
|
|
#### Mihaiii/qa-assistant-2 |
|
|
|
* Dataset: [Mihaiii/qa-assistant-2](https://huggingface.co/datasets/Mihaiii/qa-assistant-2) at [9650e69](https://huggingface.co/datasets/Mihaiii/qa-assistant-2/tree/9650e69ae0a030fa74a8706a20a168a613c43241) |
|
* Size: 3,879 evaluation samples |
|
* Columns: <code>question</code>, <code>answer</code>, and <code>score</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | question | answer | score | |
|
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------| |
|
| type | string | string | float | |
|
| details | <ul><li>min: 7 tokens</li><li>mean: 12.71 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 22.63 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 0.02</li><li>mean: 0.53</li><li>max: 1.0</li></ul> | |
|
* Samples: |
|
| question | answer | score | |
|
|:-------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------| |
|
| <code>What is the concept of social stratification?</code> | <code>The study of social stratification involves examining the inequalities and divisions within a society.</code> | <code>0.6799999999999999</code> | |
|
| <code>How does J.K. Rowling develop the character of Hermione Granger throughout the 'Harry Potter' series?</code> | <code>The 'Harry Potter' series consists of seven books, starting with 'Harry Potter and the Philosopher's Stone' and ending with 'Harry Potter and the Deathly Hallows'.</code> | <code>0.22000000000000003</code> | |
|
| <code>What is the parliamentary system and how does it function?</code> | <code>In a parliamentary system, the government can be dissolved by a vote of no confidence, which can lead to new elections.</code> | <code>0.6799999999999999</code> | |
|
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: |
|
```json |
|
{ |
|
"loss_fct": "torch.nn.modules.loss.MSELoss" |
|
} |
|
``` |
|
|
|
### Training Hyperparameters |
|
#### Non-Default Hyperparameters |
|
|
|
- `eval_strategy`: steps |
|
- `per_device_train_batch_size`: 16 |
|
- `per_device_eval_batch_size`: 16 |
|
- `num_train_epochs`: 4 |
|
- `warmup_ratio`: 0.1 |
|
|
|
#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
|
- `overwrite_output_dir`: False |
|
- `do_predict`: False |
|
- `eval_strategy`: steps |
|
- `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`: 5e-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`: False |
|
- `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 |
|
- `batch_sampler`: batch_sampler |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
| Epoch | Step | Training Loss | loss | sts-dev_spearman_cosine | |
|
|:------:|:----:|:-------------:|:------:|:-----------------------:| |
|
| 0.0999 | 100 | 0.0593 | 0.0540 | 0.5848 | |
|
| 0.1998 | 200 | 0.05 | 0.0463 | 0.6618 | |
|
| 0.2997 | 300 | 0.044 | 0.0418 | 0.7102 | |
|
| 0.3996 | 400 | 0.0413 | 0.0385 | 0.7390 | |
|
| 0.4995 | 500 | 0.0377 | 0.0349 | 0.7707 | |
|
| 0.5994 | 600 | 0.034 | 0.0333 | 0.7770 | |
|
| 0.6993 | 700 | 0.0344 | 0.0321 | 0.7879 | |
|
| 0.7992 | 800 | 0.0324 | 0.0311 | 0.7927 | |
|
| 0.8991 | 900 | 0.0334 | 0.0302 | 0.8005 | |
|
| 0.9990 | 1000 | 0.0304 | 0.0305 | 0.8023 | |
|
| 1.0989 | 1100 | 0.0261 | 0.0306 | 0.8072 | |
|
| 1.1988 | 1200 | 0.0267 | 0.0292 | 0.8104 | |
|
| 1.2987 | 1300 | 0.0244 | 0.0287 | 0.8110 | |
|
| 1.3986 | 1400 | 0.0272 | 0.0294 | 0.8098 | |
|
| 1.4985 | 1500 | 0.0241 | 0.0281 | 0.8135 | |
|
| 1.5984 | 1600 | 0.0253 | 0.0282 | 0.8143 | |
|
| 1.6983 | 1700 | 0.0245 | 0.0276 | 0.8169 | |
|
| 1.7982 | 1800 | 0.025 | 0.0274 | 0.8182 | |
|
| 1.8981 | 1900 | 0.0236 | 0.0273 | 0.8193 | |
|
| 1.9980 | 2000 | 0.0236 | 0.0269 | 0.8218 | |
|
| 2.0979 | 2100 | 0.0215 | 0.0278 | 0.8213 | |
|
| 2.1978 | 2200 | 0.0216 | 0.0269 | 0.8226 | |
|
| 2.2977 | 2300 | 0.0205 | 0.0276 | 0.8207 | |
|
| 2.3976 | 2400 | 0.0181 | 0.0273 | 0.8202 | |
|
| 2.4975 | 2500 | 0.0197 | 0.0267 | 0.8228 | |
|
| 2.5974 | 2600 | 0.02 | 0.0267 | 0.8238 | |
|
| 2.6973 | 2700 | 0.0203 | 0.0263 | 0.8258 | |
|
| 2.7972 | 2800 | 0.0184 | 0.0263 | 0.8264 | |
|
| 2.8971 | 2900 | 0.0201 | 0.0269 | 0.8243 | |
|
| 2.9970 | 3000 | 0.0196 | 0.0263 | 0.8251 | |
|
| 3.0969 | 3100 | 0.0168 | 0.0264 | 0.8250 | |
|
| 3.1968 | 3200 | 0.0176 | 0.0263 | 0.8267 | |
|
| 3.2967 | 3300 | 0.0168 | 0.0263 | 0.8270 | |
|
| 3.3966 | 3400 | 0.017 | 0.0260 | 0.8277 | |
|
| 3.4965 | 3500 | 0.0164 | 0.0261 | 0.8273 | |
|
| 3.5964 | 3600 | 0.0172 | 0.0259 | 0.8280 | |
|
| 3.6963 | 3700 | 0.0168 | 0.0260 | 0.8274 | |
|
| 3.7962 | 3800 | 0.0176 | 0.0262 | 0.8279 | |
|
| 3.8961 | 3900 | 0.0182 | 0.0261 | 0.8278 | |
|
| 3.9960 | 4000 | 0.0174 | 0.0260 | 0.8277 | |
|
|
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.0.1 |
|
- Transformers: 4.41.2 |
|
- PyTorch: 2.0.1+cu118 |
|
- Accelerate: 0.31.0 |
|
- Datasets: 2.20.0 |
|
- Tokenizers: 0.19.1 |
|
|
|
## Citation |
|
|
|
### BibTeX |
|
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#### Sentence Transformers |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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