| --- |
| language: |
| - en |
| library_name: sentence-transformers |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - generated_from_trainer |
| - dataset_size:3000 |
| - loss:MultipleNegativesRankingLoss |
| base_model: distilbert/distilroberta-base |
| datasets: |
| - sentence-transformers/all-nli |
| metrics: |
| - pearson_cosine |
| - spearman_cosine |
| - pearson_manhattan |
| - spearman_manhattan |
| - pearson_euclidean |
| - spearman_euclidean |
| - pearson_dot |
| - spearman_dot |
| - pearson_max |
| - spearman_max |
| widget: |
| - source_sentence: An Indian woman is washing and cleaning dirty laundry at a lake |
| and in the background is a kid who appears to have jumped into the lake. |
| sentences: |
| - An Indian woman is doing her laundry in a lake. |
| - An Indian woman is putting her laundry into the machine. |
| - A girl is playing with a Slinky. |
| - source_sentence: Nine women in white robes with hoods walk on plush, green grass. |
| sentences: |
| - The women each have one head. |
| - Two friends sitting on step at their job. |
| - The woman is alone and asleep in her bedroom. |
| - source_sentence: Under a blue sky with white clouds, a child reaches up to touch |
| the propeller of a plane standing parked on a field of grass. |
| sentences: |
| - A child is reaching to touch the propeller of a plane. |
| - The boy is sitting |
| - A child is playing with a ball. |
| - source_sentence: A man and a woman are talking in a park |
| sentences: |
| - A man is heading to his house of worship. |
| - A pair of people are talking outdoors. |
| - A man and woman are talking in the aquarium. |
| - source_sentence: A man running a marathon talks to his friend. |
| sentences: |
| - People watching hot air balloons inflating. |
| - There is a man running. |
| - There are people canoeing down a river. |
| pipeline_tag: sentence-similarity |
| model-index: |
| - name: SentenceTransformer based on distilbert/distilroberta-base |
| results: |
| - task: |
| type: semantic-similarity |
| name: Semantic Similarity |
| dataset: |
| name: sts dev |
| type: sts-dev |
| metrics: |
| - type: pearson_cosine |
| value: 0.7444932434233196 |
| name: Pearson Cosine |
| - type: spearman_cosine |
| value: 0.7769282355085634 |
| name: Spearman Cosine |
| - type: pearson_manhattan |
| value: 0.7502489213535852 |
| name: Pearson Manhattan |
| - type: spearman_manhattan |
| value: 0.7574428535049513 |
| name: Spearman Manhattan |
| - type: pearson_euclidean |
| value: 0.752089041601621 |
| name: Pearson Euclidean |
| - type: spearman_euclidean |
| value: 0.7583983155030144 |
| name: Spearman Euclidean |
| - type: pearson_dot |
| value: 0.49365896310259416 |
| name: Pearson Dot |
| - type: spearman_dot |
| value: 0.49513705166832495 |
| name: Spearman Dot |
| - type: pearson_max |
| value: 0.752089041601621 |
| name: Pearson Max |
| - type: spearman_max |
| value: 0.7769282355085634 |
| name: Spearman Max |
| - task: |
| type: semantic-similarity |
| name: Semantic Similarity |
| dataset: |
| name: sts test |
| type: sts-test |
| metrics: |
| - type: pearson_cosine |
| value: 0.7101248020205797 |
| name: Pearson Cosine |
| - type: spearman_cosine |
| value: 0.7072744861979087 |
| name: Spearman Cosine |
| - type: pearson_manhattan |
| value: 0.7133109440593921 |
| name: Pearson Manhattan |
| - type: spearman_manhattan |
| value: 0.6966728374126535 |
| name: Spearman Manhattan |
| - type: pearson_euclidean |
| value: 0.7142547715068376 |
| name: Pearson Euclidean |
| - type: spearman_euclidean |
| value: 0.6959833440145297 |
| name: Spearman Euclidean |
| - type: pearson_dot |
| value: 0.4503698330540162 |
| name: Pearson Dot |
| - type: spearman_dot |
| value: 0.43425556993054526 |
| name: Spearman Dot |
| - type: pearson_max |
| value: 0.7142547715068376 |
| name: Pearson Max |
| - type: spearman_max |
| value: 0.7072744861979087 |
| name: Spearman Max |
| --- |
| |
| # SentenceTransformer based on distilbert/distilroberta-base |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on the [sentence-transformers/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:** [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) <!-- at revision fb53ab8802853c8e4fbdbcd0529f21fc6f459b2b --> |
| - **Maximum Sequence Length:** 512 tokens |
| - **Output Dimensionality:** 768 tokens |
| - **Similarity Function:** Cosine Similarity |
| - **Training Dataset:** |
| - [sentence-transformers/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': 512, '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}) |
| ) |
| ``` |
|
|
| ## 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("trbeers/distilroberta-base-nli-v2") |
| # Run inference |
| sentences = [ |
| 'A man running a marathon talks to his friend.', |
| 'There is a man running.', |
| 'There are people canoeing down a river.', |
| ] |
| 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.* |
| --> |
|
|
| ## 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 | 0.7445 | |
| | **spearman_cosine** | **0.7769** | |
| | pearson_manhattan | 0.7502 | |
| | spearman_manhattan | 0.7574 | |
| | pearson_euclidean | 0.7521 | |
| | spearman_euclidean | 0.7584 | |
| | pearson_dot | 0.4937 | |
| | spearman_dot | 0.4951 | |
| | pearson_max | 0.7521 | |
| | spearman_max | 0.7769 | |
| |
| #### Semantic Similarity |
| * Dataset: `sts-test` |
| * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
| |
| | Metric | Value | |
| |:--------------------|:-----------| |
| | pearson_cosine | 0.7101 | |
| | **spearman_cosine** | **0.7073** | |
| | pearson_manhattan | 0.7133 | |
| | spearman_manhattan | 0.6967 | |
| | pearson_euclidean | 0.7143 | |
| | spearman_euclidean | 0.696 | |
| | pearson_dot | 0.4504 | |
| | spearman_dot | 0.4343 | |
| | pearson_max | 0.7143 | |
| | spearman_max | 0.7073 | |
| |
| <!-- |
| ## 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 |
| |
| #### sentence-transformers/all-nli |
| |
| * Dataset: [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) |
| * Size: 3,000 training samples |
| * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | anchor | positive | negative | |
| |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| |
| | type | string | string | string | |
| | details | <ul><li>min: 7 tokens</li><li>mean: 10.38 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.8 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.4 tokens</li><li>max: 50 tokens</li></ul> | |
| * Samples: |
| | anchor | positive | negative | |
| |:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------| |
| | <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is outdoors, on a horse.</code> | <code>A person is at a diner, ordering an omelette.</code> | |
| | <code>Children smiling and waving at camera</code> | <code>There are children present</code> | <code>The kids are frowning</code> | |
| | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>The boy skates down the sidewalk.</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 |
| |
| #### sentence-transformers/all-nli |
| |
| * Dataset: [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) |
| * Size: 300 evaluation samples |
| * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | anchor | positive | negative | |
| |:--------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
| | type | string | string | string | |
| | details | <ul><li>min: 6 tokens</li><li>mean: 18.06 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 9.8 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.44 tokens</li><li>max: 29 tokens</li></ul> | |
| * Samples: |
| | anchor | positive | negative | |
| |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:--------------------------------------------------------| |
| | <code>Two women are embracing while holding to go packages.</code> | <code>Two woman are holding packages.</code> | <code>The men are fighting outside a deli.</code> | |
| | <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> | <code>Two kids in numbered jerseys wash their hands.</code> | <code>Two kids in jackets walk to school.</code> | |
| | <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code> | <code>A man selling donuts to a customer.</code> | <code>A woman drinks her coffee in a small cafe.</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 |
| - `num_train_epochs`: 1 |
| - `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 |
| - `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`: 1 |
| - `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`: no_duplicates |
| - `multi_dataset_batch_sampler`: proportional |
| |
| </details> |
| |
| ### Training Logs |
| | Epoch | Step | loss | sts-dev_spearman_cosine | sts-test_spearman_cosine | |
| |:------:|:----:|:------:|:-----------------------:|:------------------------:| |
| | 0 | 0 | - | 0.6375 | - | |
| | 0.4167 | 10 | 2.2687 | 0.7713 | - | |
| | 0.8333 | 20 | 1.8101 | 0.7769 | - | |
| | 1.0 | 24 | - | - | 0.7073 | |
| |
| |
| ### Framework Versions |
| - Python: 3.10.11 |
| - Sentence Transformers: 3.0.1 |
| - Transformers: 4.41.2 |
| - PyTorch: 2.3.1 |
| - Accelerate: 0.31.0 |
| - Datasets: 2.19.1 |
| - 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", |
| } |
| ``` |
| |
| #### 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} |
| } |
| ``` |
| |
| <!-- |
| ## Glossary |
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| *Clearly define terms in order to be accessible across audiences.* |
| --> |
| |
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| ## Model Card Authors |
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| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
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| ## Model Card Contact |
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| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
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