| --- |
| language: |
| - en |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - generated_from_trainer |
| - dataset_size:942069 |
| - loss:MultipleNegativesRankingLoss |
| base_model: FacebookAI/roberta-base |
| widget: |
| - source_sentence: Two women having drinks and smoking cigarettes at the bar. |
| sentences: |
| - Women are celebrating at a bar. |
| - Two kids are outdoors. |
| - The four girls are attending the street festival. |
| - source_sentence: Two male police officers on patrol, wearing the normal gear and |
| bright green reflective shirts. |
| sentences: |
| - The officers have shot an unarmed black man and will not go to prison for it. |
| - The four girls are playing card games at the table. |
| - A woman is playing with a toddler. |
| - source_sentence: 5 women sitting around a table doing some crafts. |
| sentences: |
| - The girl wearing a dress skips down the sidewalk. |
| - The kids are together. |
| - Five men stand on chairs. |
| - source_sentence: Three men look on as two other men carve up a freshly barbecued |
| hog in the backyard. |
| sentences: |
| - A group of people prepare cars for racing. |
| - There are men watching others prepare food |
| - They are both waiting for a bus. |
| - source_sentence: The little boy is jumping into a puddle on the street. |
| sentences: |
| - A man is wearing a black shirt |
| - The dog is playing with a ball. |
| - The boy is outside. |
| datasets: |
| - sentence-transformers/all-nli |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| --- |
| |
| # SentenceTransformer based on FacebookAI/roberta-base |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on the [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset. 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:** [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) <!-- at revision e2da8e2f811d1448a5b465c236feacd80ffbac7b --> |
| - **Maximum Sequence Length:** 256 tokens |
| - **Output Dimensionality:** 768 dimensions |
| - **Similarity Function:** Cosine Similarity |
| - **Training Dataset:** |
| - [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) |
| - **Language:** en |
| <!-- - **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': 256, 'do_lower_case': False}) with Transformer model: RobertaModel |
| (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("sentence_transformers_model_id") |
| # Run inference |
| sentences = [ |
| 'The little boy is jumping into a puddle on the street.', |
| 'The boy is outside.', |
| 'The dog is playing with a ball.', |
| ] |
| 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 Dataset |
|
|
| #### all-nli |
|
|
| * Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) |
| * Size: 942,069 training samples |
| * Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | premise | hypothesis | label | |
| |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------| |
| | type | string | string | int | |
| | details | <ul><li>min: 6 tokens</li><li>mean: 17.4 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.69 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>0: ~33.40%</li><li>1: ~33.30%</li><li>2: ~33.30%</li></ul> | |
| * Samples: |
| | premise | hypothesis | label | |
| |:--------------------------------------------------------------------|:---------------------------------------------------------------|:---------------| |
| | <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is training his horse for a competition.</code> | <code>1</code> | |
| | <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is at a diner, ordering an omelette.</code> | <code>2</code> | |
| | <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is outdoors, on a horse.</code> | <code>0</code> | |
| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
| ```json |
| { |
| "scale": 20.0, |
| "similarity_fct": "cos_sim" |
| } |
| ``` |
|
|
| ### Evaluation Dataset |
|
|
| #### all-nli |
|
|
| * Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) |
| * Size: 19,657 evaluation samples |
| * Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | premise | hypothesis | label | |
| |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------| |
| | type | string | string | int | |
| | details | <ul><li>min: 6 tokens</li><li>mean: 18.46 tokens</li><li>max: 60 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.57 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>0: ~33.10%</li><li>1: ~33.30%</li><li>2: ~33.60%</li></ul> | |
| * Samples: |
| | premise | hypothesis | label | |
| |:-------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------|:---------------| |
| | <code>Two women are embracing while holding to go packages.</code> | <code>The sisters are hugging goodbye while holding to go packages after just eating lunch.</code> | <code>1</code> | |
| | <code>Two women are embracing while holding to go packages.</code> | <code>Two woman are holding packages.</code> | <code>0</code> | |
| | <code>Two women are embracing while holding to go packages.</code> | <code>The men are fighting outside a deli.</code> | <code>2</code> | |
| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
| ```json |
| { |
| "scale": 20.0, |
| "similarity_fct": "cos_sim" |
| } |
| ``` |
|
|
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
|
|
| - `eval_strategy`: steps |
| - `per_device_train_batch_size`: 128 |
| - `per_device_eval_batch_size`: 128 |
| - `learning_rate`: 1e-05 |
| - `warmup_ratio`: 0.1 |
| - `batch_sampler`: no_duplicates |
| |
| #### 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`: 128 |
| - `per_device_eval_batch_size`: 128 |
| - `per_gpu_train_batch_size`: None |
| - `per_gpu_eval_batch_size`: None |
| - `gradient_accumulation_steps`: 1 |
| - `eval_accumulation_steps`: None |
| - `torch_empty_cache_steps`: None |
| - `learning_rate`: 1e-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`: 3 |
| - `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`: None |
| - `hub_always_push`: False |
| - `gradient_checkpointing`: False |
| - `gradient_checkpointing_kwargs`: None |
| - `include_inputs_for_metrics`: False |
| - `include_for_metrics`: [] |
| - `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 |
| - `use_liger_kernel`: False |
| - `eval_use_gather_object`: False |
| - `average_tokens_across_devices`: False |
| - `prompts`: None |
| - `batch_sampler`: no_duplicates |
| - `multi_dataset_batch_sampler`: proportional |
|
|
| </details> |
|
|
| ### Training Logs |
| | Epoch | Step | Training Loss | Validation Loss | |
| |:------:|:----:|:-------------:|:---------------:| |
| | 0.0007 | 5 | - | 4.4994 | |
| | 0.0014 | 10 | - | 4.4981 | |
| | 0.0020 | 15 | - | 4.4960 | |
| | 0.0027 | 20 | - | 4.4930 | |
| | 0.0034 | 25 | - | 4.4890 | |
| | 0.0041 | 30 | - | 4.4842 | |
| | 0.0048 | 35 | - | 4.4784 | |
| | 0.0054 | 40 | - | 4.4716 | |
| | 0.0061 | 45 | - | 4.4636 | |
| | 0.0068 | 50 | - | 4.4543 | |
| | 0.0075 | 55 | - | 4.4438 | |
| | 0.0082 | 60 | - | 4.4321 | |
| | 0.0088 | 65 | - | 4.4191 | |
| | 0.0095 | 70 | - | 4.4042 | |
| | 0.0102 | 75 | - | 4.3875 | |
| | 0.0109 | 80 | - | 4.3686 | |
| | 0.0115 | 85 | - | 4.3474 | |
| | 0.0122 | 90 | - | 4.3236 | |
| | 0.0129 | 95 | - | 4.2968 | |
| | 0.0136 | 100 | 4.4995 | 4.2666 | |
| | 0.0143 | 105 | - | 4.2326 | |
| | 0.0149 | 110 | - | 4.1947 | |
| | 0.0156 | 115 | - | 4.1516 | |
| | 0.0163 | 120 | - | 4.1029 | |
| | 0.0170 | 125 | - | 4.0476 | |
| | 0.0177 | 130 | - | 3.9850 | |
| | 0.0183 | 135 | - | 3.9162 | |
| | 0.0190 | 140 | - | 3.8397 | |
| | 0.0197 | 145 | - | 3.7522 | |
| | 0.0204 | 150 | - | 3.6521 | |
| | 0.0211 | 155 | - | 3.5388 | |
| | 0.0217 | 160 | - | 3.4114 | |
| | 0.0224 | 165 | - | 3.2701 | |
| | 0.0231 | 170 | - | 3.1147 | |
| | 0.0238 | 175 | - | 2.9471 | |
| | 0.0245 | 180 | - | 2.7710 | |
| | 0.0251 | 185 | - | 2.5909 | |
| | 0.0258 | 190 | - | 2.4127 | |
| | 0.0265 | 195 | - | 2.2439 | |
| | 0.0272 | 200 | 3.6918 | 2.0869 | |
|
|
|
|
| ### Framework Versions |
| - Python: 3.12.8 |
| - Sentence Transformers: 3.4.1 |
| - Transformers: 4.48.3 |
| - PyTorch: 2.2.0+cu121 |
| - Accelerate: 1.3.0 |
| - Datasets: 3.2.0 |
| - Tokenizers: 0.21.0 |
|
|
| ## 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", |
| } |
| ``` |
|
|
| #### MultipleNegativesRankingLoss |
| ```bibtex |
| @misc{henderson2017efficient, |
| title={Efficient Natural Language Response Suggestion for Smart Reply}, |
| author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
| year={2017}, |
| eprint={1705.00652}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CL} |
| } |
| ``` |
|
|
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| *Clearly define terms in order to be accessible across audiences.* |
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