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--- |
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language: |
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- en |
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library_name: sentence-transformers |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- dataset_size:100K<n<1M |
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- loss:CachedMultipleNegativesRankingLoss |
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base_model: Unbabel/xlm-roberta-comet-small |
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metrics: |
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- cosine_accuracy |
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- dot_accuracy |
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- manhattan_accuracy |
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- euclidean_accuracy |
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- max_accuracy |
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widget: |
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- source_sentence: There's a dock |
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sentences: |
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- There is a door. |
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- the animal is running |
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- The woman is singing. |
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- source_sentence: The boy scowls |
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sentences: |
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- A boy is blowing bubbles. |
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- He is playing a song. |
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- They are driving cars. |
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- source_sentence: A bird flying. |
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sentences: |
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- A butterfly flys freely. |
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- A dog carries a bone. |
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- Two dogs are playing. |
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- source_sentence: A woman sings. |
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sentences: |
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- The woman is singing. |
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- The man is in a city. |
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- there is a man in a pool. |
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- source_sentence: a baby smiling |
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sentences: |
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- A baby is unhappy. |
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- The dog has big ears. |
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- They are driving cars. |
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pipeline_tag: sentence-similarity |
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model-index: |
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- name: SentenceTransformer based on Unbabel/xlm-roberta-comet-small |
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results: |
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- task: |
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type: triplet |
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name: Triplet |
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dataset: |
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name: all nli dev |
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type: all-nli-dev |
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metrics: |
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- type: cosine_accuracy |
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value: 0.849 |
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name: Cosine Accuracy |
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- type: dot_accuracy |
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value: 0.163 |
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name: Dot Accuracy |
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- type: manhattan_accuracy |
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value: 0.837 |
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name: Manhattan Accuracy |
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- type: euclidean_accuracy |
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value: 0.841 |
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name: Euclidean Accuracy |
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- type: max_accuracy |
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value: 0.849 |
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name: Max Accuracy |
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- task: |
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type: triplet |
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name: Triplet |
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dataset: |
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name: all nli test |
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type: all-nli-test |
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metrics: |
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- type: cosine_accuracy |
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value: 0.839 |
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name: Cosine Accuracy |
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- type: dot_accuracy |
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value: 0.15 |
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name: Dot Accuracy |
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- type: manhattan_accuracy |
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value: 0.827 |
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name: Manhattan Accuracy |
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- type: euclidean_accuracy |
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value: 0.827 |
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name: Euclidean Accuracy |
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- type: max_accuracy |
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value: 0.839 |
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name: Max Accuracy |
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--- |
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|
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# SentenceTransformer based on Unbabel/xlm-roberta-comet-small |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Unbabel/xlm-roberta-comet-small](https://huggingface.co/Unbabel/xlm-roberta-comet-small) on the [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) 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 |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [Unbabel/xlm-roberta-comet-small](https://huggingface.co/Unbabel/xlm-roberta-comet-small) <!-- at revision df568a015df5cefbf2f449314b61ce9afb0cb593 --> |
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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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- [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) |
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- **Language:** en |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel |
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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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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("mics-nlp/xlm-roberta-small-all-nli-triplet") |
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# Run inference |
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sentences = [ |
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'a baby smiling', |
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'A baby is unhappy.', |
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'The dog has big ears.', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 384] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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## Evaluation |
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### Metrics |
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#### Triplet |
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* Dataset: `all-nli-dev` |
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* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) |
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| Metric | Value | |
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|:-------------------|:----------| |
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| cosine_accuracy | 0.849 | |
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| dot_accuracy | 0.163 | |
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| manhattan_accuracy | 0.837 | |
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| euclidean_accuracy | 0.841 | |
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| **max_accuracy** | **0.849** | |
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#### Triplet |
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* Dataset: `all-nli-test` |
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* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) |
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| Metric | Value | |
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|:-------------------|:----------| |
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| cosine_accuracy | 0.839 | |
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| dot_accuracy | 0.15 | |
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| manhattan_accuracy | 0.827 | |
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| euclidean_accuracy | 0.827 | |
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| **max_accuracy** | **0.839** | |
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<!-- |
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## Bias, Risks and Limitations |
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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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--> |
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## Training Details |
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### Training Dataset |
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#### sentence-transformers/all-nli |
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* 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) |
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* Size: 100,000 training samples |
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* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | anchor | positive | negative | |
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|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | string | |
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| details | <ul><li>min: 7 tokens</li><li>mean: 10.9 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.62 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.76 tokens</li><li>max: 55 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | negative | |
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|:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------| |
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| <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> | |
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| <code>Children smiling and waving at camera</code> | <code>There are children present</code> | <code>The kids are frowning</code> | |
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| <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> | |
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* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim" |
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} |
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``` |
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### Evaluation Dataset |
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#### sentence-transformers/all-nli |
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* 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) |
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* Size: 1,000 evaluation samples |
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* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | anchor | positive | negative | |
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|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | string | |
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| details | <ul><li>min: 6 tokens</li><li>mean: 20.31 tokens</li><li>max: 83 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.71 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 11.39 tokens</li><li>max: 32 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | negative | |
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|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:--------------------------------------------------------| |
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| <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> | |
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| <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> | |
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| <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> | |
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* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim" |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `num_train_epochs`: 1 |
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- `warmup_ratio`: 0.1 |
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- `bf16`: True |
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- `batch_sampler`: no_duplicates |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: steps |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `learning_rate`: 5e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 1 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: True |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: False |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: proportional |
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|
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</details> |
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### Training Logs |
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| Epoch | Step | Training Loss | loss | all-nli-dev_max_accuracy | all-nli-test_max_accuracy | |
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|:-----:|:----:|:-------------:|:------:|:------------------------:|:-------------------------:| |
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| 0 | 0 | - | - | 0.541 | - | |
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| 0.016 | 100 | 3.5308 | 3.1817 | 0.558 | - | |
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| 0.032 | 200 | 3.2784 | 3.0406 | 0.597 | - | |
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| 0.048 | 300 | 3.113 | 2.7572 | 0.635 | - | |
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| 0.064 | 400 | 2.8296 | 2.4646 | 0.68 | - | |
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| 0.08 | 500 | 2.631 | 2.3583 | 0.676 | - | |
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| 0.096 | 600 | 2.3247 | 2.1394 | 0.706 | - | |
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| 0.112 | 700 | 2.2211 | 2.0201 | 0.711 | - | |
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| 0.128 | 800 | 2.1263 | 1.9560 | 0.757 | - | |
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| 0.144 | 900 | 2.2105 | 1.9074 | 0.748 | - | |
|
| 0.16 | 1000 | 2.0637 | 1.9289 | 0.728 | - | |
|
| 0.176 | 1100 | 2.1772 | 1.8796 | 0.741 | - | |
|
| 0.192 | 1200 | 2.1518 | 1.8346 | 0.761 | - | |
|
| 0.208 | 1300 | 1.728 | 1.8213 | 0.765 | - | |
|
| 0.224 | 1400 | 1.8101 | 1.6321 | 0.772 | - | |
|
| 0.24 | 1500 | 1.7516 | 1.5669 | 0.793 | - | |
|
| 0.256 | 1600 | 1.4988 | 1.5538 | 0.8 | - | |
|
| 0.272 | 1700 | 1.6695 | 1.5462 | 0.803 | - | |
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| 0.288 | 1800 | 1.5971 | 1.5499 | 0.783 | - | |
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| 0.304 | 1900 | 1.5614 | 1.5047 | 0.788 | - | |
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| 0.32 | 2000 | 1.522 | 1.4957 | 0.794 | - | |
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| 0.336 | 2100 | 1.3624 | 1.4153 | 0.814 | - | |
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| 0.352 | 2200 | 1.4773 | 1.4169 | 0.809 | - | |
|
| 0.368 | 2300 | 1.6066 | 1.3697 | 0.813 | - | |
|
| 0.384 | 2400 | 1.5106 | 1.3203 | 0.819 | - | |
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| 0.4 | 2500 | 1.4783 | 1.3417 | 0.817 | - | |
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| 0.416 | 2600 | 1.3696 | 1.2650 | 0.824 | - | |
|
| 0.432 | 2700 | 1.5115 | 1.2779 | 0.829 | - | |
|
| 0.448 | 2800 | 1.4834 | 1.2668 | 0.834 | - | |
|
| 0.464 | 2900 | 1.4823 | 1.2621 | 0.836 | - | |
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| 0.48 | 3000 | 1.4163 | 1.2465 | 0.837 | - | |
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| 0.496 | 3100 | 1.4232 | 1.2475 | 0.837 | - | |
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| 0.512 | 3200 | 1.2193 | 1.1975 | 0.838 | - | |
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| 0.528 | 3300 | 1.2569 | 1.1816 | 0.838 | - | |
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| 0.544 | 3400 | 1.2988 | 1.1936 | 0.839 | - | |
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| 0.56 | 3500 | 1.5068 | 1.2213 | 0.835 | - | |
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| 0.576 | 3600 | 1.3022 | 1.1799 | 0.842 | - | |
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| 0.592 | 3700 | 1.3823 | 1.1910 | 0.831 | - | |
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| 0.608 | 3800 | 1.4224 | 1.1786 | 0.834 | - | |
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| 0.624 | 3900 | 1.3765 | 1.1541 | 0.843 | - | |
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| 0.64 | 4000 | 1.4987 | 1.1365 | 0.844 | - | |
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| 0.656 | 4100 | 1.7525 | 1.1394 | 0.843 | - | |
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| 0.672 | 4200 | 1.6013 | 1.1178 | 0.841 | - | |
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| 0.688 | 4300 | 1.3326 | 1.0959 | 0.846 | - | |
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| 0.704 | 4400 | 1.355 | 1.0757 | 0.848 | - | |
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| 0.72 | 4500 | 1.2834 | 1.0681 | 0.846 | - | |
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| 0.736 | 4600 | 1.2939 | 1.0696 | 0.85 | - | |
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| 0.752 | 4700 | 1.4069 | 1.0645 | 0.848 | - | |
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| 0.768 | 4800 | 1.4503 | 1.0609 | 0.849 | - | |
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| 0.784 | 4900 | 1.2833 | 1.0587 | 0.847 | - | |
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| 0.8 | 5000 | 1.3321 | 1.0563 | 0.849 | - | |
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| 0.816 | 5100 | 1.3006 | 1.0539 | 0.847 | - | |
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| 0.832 | 5200 | 1.4332 | 1.0527 | 0.847 | - | |
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| 0.848 | 5300 | 1.3101 | 1.0505 | 0.848 | - | |
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| 0.864 | 5400 | 1.3658 | 1.0523 | 0.849 | - | |
|
| 0.88 | 5500 | 1.353 | 1.0520 | 0.849 | - | |
|
| 0.896 | 5600 | 1.2429 | 1.0521 | 0.848 | - | |
|
| 0.912 | 5700 | 1.3512 | 1.0505 | 0.848 | - | |
|
| 0.928 | 5800 | 1.2995 | 1.0501 | 0.848 | - | |
|
| 0.944 | 5900 | 1.3514 | 1.0491 | 0.849 | - | |
|
| 0.96 | 6000 | 1.3976 | 1.0490 | 0.848 | - | |
|
| 0.976 | 6100 | 1.2112 | 1.0487 | 0.848 | - | |
|
| 0.992 | 6200 | 0.0033 | 1.0492 | 0.849 | - | |
|
| 1.0 | 6250 | - | - | - | 0.839 | |
|
|
|
|
|
### Framework Versions |
|
- Python: 3.9.10 |
|
- Sentence Transformers: 3.0.0 |
|
- Transformers: 4.41.2 |
|
- PyTorch: 2.3.0+cu121 |
|
- Accelerate: 0.26.1 |
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- Datasets: 2.16.1 |
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- Tokenizers: 0.19.1 |
|
|
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## Citation |
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### BibTeX |
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|
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#### Sentence Transformers |
|
```bibtex |
|
@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", |
|
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", |
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} |
|
``` |
|
|
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#### CachedMultipleNegativesRankingLoss |
|
```bibtex |
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@misc{gao2021scaling, |
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title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup}, |
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author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan}, |
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year={2021}, |
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eprint={2101.06983}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.LG} |
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} |
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``` |
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