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--- |
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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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- generated_from_trainer |
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- dataset_size:1441905 |
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- loss:CachedMultipleNegativesRankingLoss |
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widget: |
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- source_sentence: Treponema caused disease or disorder |
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sentences: |
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- bejel |
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- tumor of ureter |
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- debrisoquine, ultrarapid metabolism of |
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- source_sentence: B cell (antibody) deficiencies |
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sentences: |
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- distal phalanx of digit IV |
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- well-differentiated fetal adenocarcinoma of the lung |
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- deficiency of humoral immunity |
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- source_sentence: Elevated AdoHcy concentration |
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sentences: |
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- gepulste Abgabe |
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- Elevated circulating S-adenosyl-L-homocysteine concentration |
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- Frequently cries for no reason |
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- source_sentence: Isoelectric focusing of serum transferrin consistent with CDG type |
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II |
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sentences: |
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- Amblyomma aureolatum |
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- squamous cell carcinoma of the bile duct |
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- Abnormal isoelectric focusing of serum transferrin, type 2 pattern |
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- source_sentence: Light-chain amyloidosis |
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sentences: |
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- partial deletion of the long arm of chromosome X |
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- Teneria teneriensis |
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- amyloidosis primary systemic |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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metrics: |
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- cosine_accuracy@1 |
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- cosine_accuracy@3 |
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- cosine_accuracy@5 |
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- cosine_accuracy@10 |
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- cosine_precision@1 |
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- cosine_precision@3 |
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- cosine_precision@5 |
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- cosine_precision@10 |
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- cosine_recall@1 |
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- cosine_recall@3 |
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- cosine_recall@5 |
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- cosine_recall@10 |
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- cosine_ndcg@10 |
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- cosine_mrr@10 |
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- cosine_map@100 |
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model-index: |
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- name: SentenceTransformer |
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results: |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: owl ontology eval |
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type: owl_ontology_eval |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.6302799165287473 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8147801683816651 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.8775275239260272 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.9268187378570915 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.6302799165287473 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.27634261591230724 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.17979420018709072 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.09566812981218968 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.6216929313281044 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8081120625554675 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.8723585426111152 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.9241442997289582 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.7796907170635903 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.7342337217921898 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.734065731352359 |
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name: Cosine Map@100 |
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--- |
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# SentenceTransformer |
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This is a [sentence-transformers](https://www.SBERT.net) model trained on the json 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:** [Unknown](https://huggingface.co/unknown) --> |
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- **Maximum Sequence Length:** 1024 tokens |
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- **Output Dimensionality:** 384 dimensions |
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- **Similarity Function:** Cosine Similarity |
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- **Training Dataset:** |
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- json |
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<!-- - **Language:** Unknown --> |
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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': 1024, '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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## 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("pankajrajdeo/bond-embed-v1-fp16") |
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# Run inference |
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sentences = [ |
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'Light-chain amyloidosis', |
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'amyloidosis primary systemic', |
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'partial deletion of the long arm of chromosome X', |
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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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#### Information Retrieval |
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* Dataset: `owl_ontology_eval` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
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| Metric | Value | |
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|:--------------------|:-----------| |
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| cosine_accuracy@1 | 0.6303 | |
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| cosine_accuracy@3 | 0.8148 | |
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| cosine_accuracy@5 | 0.8775 | |
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| cosine_accuracy@10 | 0.9268 | |
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| cosine_precision@1 | 0.6303 | |
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| cosine_precision@3 | 0.2763 | |
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| cosine_precision@5 | 0.1798 | |
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| cosine_precision@10 | 0.0957 | |
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| cosine_recall@1 | 0.6217 | |
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| cosine_recall@3 | 0.8081 | |
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| cosine_recall@5 | 0.8724 | |
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| cosine_recall@10 | 0.9241 | |
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| **cosine_ndcg@10** | **0.7797** | |
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| cosine_mrr@10 | 0.7342 | |
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| cosine_map@100 | 0.7341 | |
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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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#### json |
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* Dataset: json |
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* Size: 1,441,905 training samples |
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* Columns: <code>anchor</code> and <code>positive</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | anchor | positive | |
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|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 3 tokens</li><li>mean: 9.48 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 8.68 tokens</li><li>max: 30 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | |
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|:-------------------------------------|:-------------------------------------| |
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| <code>Mangshan horned toad</code> | <code>Mangshan spadefoot toad</code> | |
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| <code>Leuconotopicos borealis</code> | <code>Picoides borealis</code> | |
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| <code>Cylindrella teneriensis</code> | <code>Teneria teneriensis</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`: 1024 |
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- `learning_rate`: 1.5e-05 |
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- `num_train_epochs`: 5 |
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- `lr_scheduler_type`: cosine |
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- `warmup_ratio`: 0.05 |
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- `bf16`: True |
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- `dataloader_num_workers`: 32 |
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- `load_best_model_at_end`: True |
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- `gradient_checkpointing`: True |
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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`: 1024 |
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- `per_device_eval_batch_size`: 8 |
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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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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 1.5e-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`: 5 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: cosine |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.05 |
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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`: 32 |
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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`: True |
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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`: None |
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- `hub_always_push`: False |
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- `hub_revision`: None |
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- `gradient_checkpointing`: True |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `include_for_metrics`: [] |
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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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- `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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- `eval_on_start`: False |
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- `use_liger_kernel`: False |
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- `liger_kernel_config`: None |
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- `eval_use_gather_object`: False |
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- `average_tokens_across_devices`: False |
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- `prompts`: None |
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- `batch_sampler`: batch_sampler |
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- `multi_dataset_batch_sampler`: proportional |
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</details> |
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### Training Logs |
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| Epoch | Step | Training Loss | owl_ontology_eval_cosine_ndcg@10 | |
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|:------:|:----:|:-------------:|:--------------------------------:| |
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| 0.0717 | 100 | 1.3232 | - | |
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| 0.1434 | 200 | 1.021 | - | |
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| 0.2151 | 300 | 0.9633 | - | |
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| 0.2867 | 400 | 0.9068 | - | |
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| 0.3297 | 460 | - | 0.7207 | |
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| 0.3584 | 500 | 0.8723 | - | |
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| 0.4301 | 600 | 0.852 | - | |
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| 0.5018 | 700 | 0.8161 | - | |
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| 0.5735 | 800 | 0.7939 | - | |
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| 0.6452 | 900 | 0.7935 | - | |
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| 0.6595 | 920 | - | 0.7364 | |
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| 0.7168 | 1000 | 0.7646 | - | |
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| 0.7885 | 1100 | 0.7464 | - | |
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| 0.8602 | 1200 | 0.7376 | - | |
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| 0.9319 | 1300 | 0.7313 | - | |
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| 0.9892 | 1380 | - | 0.7468 | |
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| 1.0036 | 1400 | 0.7099 | - | |
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| 1.0753 | 1500 | 0.6884 | - | |
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| 1.1470 | 1600 | 0.6776 | - | |
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| 1.2186 | 1700 | 0.6694 | - | |
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| 1.2903 | 1800 | 0.6641 | - | |
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| 1.3190 | 1840 | - | 0.7561 | |
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| 1.3620 | 1900 | 0.6526 | - | |
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| 1.4337 | 2000 | 0.6524 | - | |
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| 1.5054 | 2100 | 0.6364 | - | |
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| 1.5771 | 2200 | 0.6339 | - | |
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| 1.6487 | 2300 | 0.626 | 0.7614 | |
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| 1.7204 | 2400 | 0.6197 | - | |
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| 1.7921 | 2500 | 0.6193 | - | |
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| 1.8638 | 2600 | 0.6155 | - | |
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| 1.9355 | 2700 | 0.6142 | - | |
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| 1.9785 | 2760 | - | 0.7662 | |
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| 2.0072 | 2800 | 0.5853 | - | |
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| 2.0789 | 2900 | 0.5824 | - | |
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| 2.1505 | 3000 | 0.5769 | - | |
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| 2.2222 | 3100 | 0.5765 | - | |
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| 2.2939 | 3200 | 0.5608 | - | |
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| 2.3082 | 3220 | - | 0.7698 | |
|
|
| 2.3656 | 3300 | 0.5695 | - | |
|
|
| 2.4373 | 3400 | 0.5641 | - | |
|
|
| 2.5090 | 3500 | 0.5638 | - | |
|
|
| 2.5806 | 3600 | 0.554 | - | |
|
|
| 2.6380 | 3680 | - | 0.7735 | |
|
|
| 2.6523 | 3700 | 0.5539 | - | |
|
|
| 2.7240 | 3800 | 0.5495 | - | |
|
|
| 2.7957 | 3900 | 0.5556 | - | |
|
|
| 2.8674 | 4000 | 0.5397 | - | |
|
|
| 2.9391 | 4100 | 0.5447 | - | |
|
|
| 2.9677 | 4140 | - | 0.7757 | |
|
|
| 3.0108 | 4200 | 0.5331 | - | |
|
|
| 3.0824 | 4300 | 0.5336 | - | |
|
|
| 3.1541 | 4400 | 0.5346 | - | |
|
|
| 3.2258 | 4500 | 0.5247 | - | |
|
|
| 3.2975 | 4600 | 0.5241 | 0.7775 | |
|
|
| 3.3692 | 4700 | 0.5257 | - | |
|
|
| 3.4409 | 4800 | 0.5241 | - | |
|
|
| 3.5125 | 4900 | 0.5171 | - | |
|
|
| 3.5842 | 5000 | 0.5215 | - | |
|
|
| 3.6272 | 5060 | - | 0.7787 | |
|
|
| 3.6559 | 5100 | 0.5203 | - | |
|
|
| 3.7276 | 5200 | 0.5214 | - | |
|
|
| 3.7993 | 5300 | 0.5266 | - | |
|
|
| 3.8710 | 5400 | 0.5127 | - | |
|
|
| 3.9427 | 5500 | 0.5062 | - | |
|
|
| 3.9570 | 5520 | - | 0.7790 | |
|
|
| 4.0143 | 5600 | 0.5104 | - | |
|
|
| 4.0860 | 5700 | 0.5155 | - | |
|
|
| 4.1577 | 5800 | 0.5042 | - | |
|
|
| 4.2294 | 5900 | 0.5174 | - | |
|
|
| 4.2867 | 5980 | - | 0.7797 | |
|
|
| 4.3011 | 6000 | 0.509 | - | |
|
|
| 4.3728 | 6100 | 0.5106 | - | |
|
|
| 4.4444 | 6200 | 0.5076 | - | |
|
|
| 4.5161 | 6300 | 0.5046 | - | |
|
|
| 4.5878 | 6400 | 0.5077 | - | |
|
|
| 4.6165 | 6440 | - | 0.7795 | |
|
|
| 4.6595 | 6500 | 0.5114 | - | |
|
|
| 4.7312 | 6600 | 0.5103 | - | |
|
|
| 4.8029 | 6700 | 0.5106 | - | |
|
|
| 4.8746 | 6800 | 0.5102 | - | |
|
|
| 4.9462 | 6900 | 0.5076 | 0.7797 | |
|
|
|
|
|
|
|
|
### Framework Versions |
|
|
- Python: 3.11.11 |
|
|
- Sentence Transformers: 3.4.1 |
|
|
- Transformers: 4.53.2 |
|
|
- PyTorch: 2.6.0+cu124 |
|
|
- Accelerate: 1.5.2 |
|
|
- Datasets: 3.2.0 |
|
|
- Tokenizers: 0.21.0 |
|
|
|
|
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## Citation |
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### BibTeX |
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|
|
|
|
#### 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", |
|
|
} |
|
|
``` |
|
|
|
|
|
#### CachedMultipleNegativesRankingLoss |
|
|
```bibtex |
|
|
@misc{gao2021scaling, |
|
|
title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup}, |
|
|
author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan}, |
|
|
year={2021}, |
|
|
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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