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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:10000 |
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- loss:ContrastiveLoss |
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base_model: DeepChem/ChemBERTa-77M-MLM |
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widget: |
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- source_sentence: CC(C)N=c1cc2n(-c3ccc(Cl)cc3)c3ccccc3nc-2cc1Nc1ccc(Cl)cc1 |
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sentences: |
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- C[NH+]1CCC(=C2c3ccccc3CCn3c(C=O)c[nH+]c32)CC1 |
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- COc1ccc(C(=O)CC(=O)c2ccc(C(C)(C)C)cc2)cc1 |
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- CC1CNc2c(cccc2S(=O)(=O)NC(CCC[NH+]=C(N)N)C(=O)N2CCC(C)CC2C(=O)[O-])C1 |
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- source_sentence: CC(C)c1ccc2oc3nc(N)c(C(=O)[O-])cc3c(=O)c2c1 |
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sentences: |
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- COC1=CC(=O)CC(C)C12Oc1c(Cl)c(OC)cc(OC)c1C2=O |
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- CON=C(C(=O)NC1C(=O)N2C(C(=O)[O-])=C(C[N+]3(C)CCCC3)CSC12)c1csc(N)n1 |
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- CC1C=CC=CC=CC=CC=CC=CC=CC(OC2OC(C)C(O)C([NH3+])C2O)CC2OC(O)(CC(O)CC(O)C(O)CCC(O)CC(O)CC(=O)OC(C)C(C)C1O)CC(O)C2C(=O)[O-] |
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- source_sentence: C[NH2+]C1CCc2[nH]c3ccc(C(N)=O)cc3c2C1 |
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sentences: |
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- CC(OC(=O)c1ccccc1)C1=CCC23OCC[NH+](C)CC12CC(O)C12OC4(O)CCC1(C)C(CC=C32)C4 |
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- CC(=O)NC(Cc1ccc2ccccc2c1)C(=O)NC(Cc1ccc(Cl)cc1)C(=O)NC(Cc1cccnc1)C(=O)NC(CO)C(=O)NC(Cc1ccc(NC(=O)C2CC(=O)NC(=O)N2)cc1)C(=O)NC(Cc1ccc(NC(N)=O)cc1)C(=O)NC(CC(C)C)C(=O)NC(CCCC[NH2+]C(C)C)C(=O)N1CCCC1C(=O)NC(C)C(N)=O |
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- C[NH+](C)CCOC(=O)C(c1ccccc1)C1(O)CCCC1 |
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- source_sentence: CC(C)n1c(C=CC(O)CC(O)CC(=O)[O-])c(-c2ccc(F)cc2)c2ccccc21 |
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sentences: |
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- C#CC1(O)CCC2C3CCC4=C(CCC(=O)C4)C3CCC21C |
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- CC(C=CC(C)C(C)(C)O)C1CCC2C(=CC=C3CC(O)CC(O)C3)CCCC21C |
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- CC(C)CNCc1ccc(-c2ccccc2S(=O)(=O)N2CCCC2)cc1 |
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- source_sentence: CC#CCn1c(N2CCCC([NH3+])C2)nc2c1c(=O)n(Cc1nc(C)c3ccccc3n1)c(=O)n2C |
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sentences: |
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- C[N+]1(C)CCCC(OC(=O)C(O)(c2ccccc2)c2ccccc2)C1 |
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- CC(Cc1ccc(O)c(O)c1)C(C)Cc1ccc(O)c(O)c1 |
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- CC12CCC(=O)C=C1CCC1C2C(O)CC2(C)C1CCC2(O)C(=O)COC(=O)CCC1CCCC1 |
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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 |
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- cosine_accuracy_threshold |
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- cosine_f1 |
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- cosine_f1_threshold |
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- cosine_precision |
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- cosine_recall |
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- cosine_ap |
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model-index: |
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- name: SentenceTransformer based on DeepChem/ChemBERTa-77M-MLM |
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results: |
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- task: |
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type: binary-classification |
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name: Binary Classification |
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dataset: |
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name: all dev |
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type: all-dev |
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metrics: |
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- type: cosine_accuracy |
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value: 0.9066 |
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name: Cosine Accuracy |
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- type: cosine_accuracy_threshold |
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value: 0.5664876699447632 |
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name: Cosine Accuracy Threshold |
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- type: cosine_f1 |
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value: 0.9510122731564041 |
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name: Cosine F1 |
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- type: cosine_f1_threshold |
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value: 0.5664876699447632 |
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name: Cosine F1 Threshold |
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- type: cosine_precision |
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value: 0.9067813562712542 |
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name: Cosine Precision |
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- type: cosine_recall |
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value: 0.9997794441993825 |
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name: Cosine Recall |
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- type: cosine_ap |
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value: 0.9523113003188102 |
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name: Cosine Ap |
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--- |
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# SentenceTransformer based on DeepChem/ChemBERTa-77M-MLM |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [DeepChem/ChemBERTa-77M-MLM](https://huggingface.co/DeepChem/ChemBERTa-77M-MLM). 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:** [DeepChem/ChemBERTa-77M-MLM](https://huggingface.co/DeepChem/ChemBERTa-77M-MLM) <!-- at revision ed8a5374f2024ec8da53760af91a33fb8f6a15ff --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 384 dimensions |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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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': 512, 'do_lower_case': False}) with Transformer model: RobertaModel |
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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("HassanCS/chemBERTa-tuned-on-ClinTox-3") |
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# Run inference |
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sentences = [ |
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'CC#CCn1c(N2CCCC([NH3+])C2)nc2c1c(=O)n(Cc1nc(C)c3ccccc3n1)c(=O)n2C', |
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'CC12CCC(=O)C=C1CCC1C2C(O)CC2(C)C1CCC2(O)C(=O)COC(=O)CCC1CCCC1', |
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'CC(Cc1ccc(O)c(O)c1)C(C)Cc1ccc(O)c(O)c1', |
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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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#### Binary Classification |
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* Dataset: `all-dev` |
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* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) |
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| Metric | Value | |
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|:--------------------------|:-----------| |
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| cosine_accuracy | 0.9066 | |
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| cosine_accuracy_threshold | 0.5665 | |
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| cosine_f1 | 0.951 | |
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| cosine_f1_threshold | 0.5665 | |
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| cosine_precision | 0.9068 | |
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| cosine_recall | 0.9998 | |
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| **cosine_ap** | **0.9523** | |
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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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#### Unnamed Dataset |
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* Size: 10,000 training samples |
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* Columns: <code>smiles1</code>, <code>smiles2</code>, and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | smiles1 | smiles2 | label | |
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|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------| |
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| type | string | string | int | |
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| details | <ul><li>min: 3 tokens</li><li>mean: 40.69 tokens</li><li>max: 221 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 51.43 tokens</li><li>max: 221 tokens</li></ul> | <ul><li>0: ~14.90%</li><li>1: ~85.10%</li></ul> | |
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* Samples: |
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| smiles1 | smiles2 | label | |
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|:----------------------------------------|:-------------------------------------------------------------|:---------------| |
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| <code>Cn1c(=O)c2c(ncn2C)n(C)c1=O</code> | <code>Cc1cc2c(s1)=Nc1ccccc1NC=2N1CC[NH+](C)CC1</code> | <code>1</code> | |
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| <code>Oc1ccc(OCc2ccccc2)cc1</code> | <code>Oc1ccc(CCCC[NH2+]CC(O)c2ccc(O)c(O)c2)cc1</code> | <code>1</code> | |
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| <code>OCC(S)CS</code> | <code>CC12CCC(=O)C=C1CCC1C2C(O)CC2(C)C1CCC2(O)C(=O)CO</code> | <code>0</code> | |
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* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters: |
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```json |
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{ |
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"distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", |
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"margin": 0.5, |
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"size_average": true |
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} |
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``` |
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### Evaluation Dataset |
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#### Unnamed Dataset |
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* Size: 5,000 evaluation samples |
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* Columns: <code>smiles1</code>, <code>smiles2</code>, and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | smiles1 | smiles2 | label | |
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|:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------| |
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| type | string | string | int | |
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| details | <ul><li>min: 18 tokens</li><li>mean: 56.96 tokens</li><li>max: 209 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 61.21 tokens</li><li>max: 244 tokens</li></ul> | <ul><li>0: ~10.00%</li><li>1: ~90.00%</li></ul> | |
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* Samples: |
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| smiles1 | smiles2 | label | |
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|:---------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------|:---------------| |
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| <code>CC(=CC(=O)OCCCCCCCCC(=O)[O-])CC1OCC(CC2OC2C(C)C(C)O)C(O)C1O</code> | <code>CC(C=CC(C)C(C)(C)O)C1CCC2C(=CC=C3CC(O)CC(O)C3)CCCC21C</code> | <code>1</code> | |
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| <code>C=C1c2cccc([O-])c2C(=O)C2=C([O-])C3(O)C(=O)C(C(N)=O)=C([O-])C([NH+](C)C)C3C(O)C12</code> | <code>CC(c1ncncc1F)C(O)(Cn1cncn1)c1ccc(F)cc1F</code> | <code>1</code> | |
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| <code>CC(C)CC1C(=O)N2CCCC2C2(O)OC(NC(=O)C3C=C4c5cccc6[nH]c(Br)c(c56)CC4[NH+](C)C3)(C(C)C)C(=O)N12</code> | <code>C[NH+](C)CCC=C1c2ccccc2Sc2ccc(Cl)cc21</code> | <code>1</code> | |
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* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters: |
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```json |
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{ |
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"distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", |
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"margin": 0.5, |
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"size_average": true |
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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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- `learning_rate`: 2e-05 |
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- `num_train_epochs`: 5 |
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- `warmup_ratio`: 0.1 |
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- `fp16`: 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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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 2e-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`: 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`: False |
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- `fp16`: True |
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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`: None |
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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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- `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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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
|
|
- `include_num_input_tokens_seen`: False |
|
|
- `neftune_noise_alpha`: None |
|
|
- `optim_target_modules`: None |
|
|
- `batch_eval_metrics`: False |
|
|
- `eval_on_start`: False |
|
|
- `use_liger_kernel`: False |
|
|
- `eval_use_gather_object`: False |
|
|
- `average_tokens_across_devices`: False |
|
|
- `prompts`: None |
|
|
- `batch_sampler`: no_duplicates |
|
|
- `multi_dataset_batch_sampler`: proportional |
|
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|
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</details> |
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|
|
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### Training Logs |
|
|
| Epoch | Step | Training Loss | Validation Loss | all-dev_cosine_ap | |
|
|
|:-----:|:----:|:-------------:|:---------------:|:-----------------:| |
|
|
| 0.8 | 500 | 0.0264 | 0.0112 | 0.9213 | |
|
|
| 1.6 | 1000 | 0.0152 | 0.0122 | 0.9362 | |
|
|
| 2.4 | 1500 | 0.0134 | 0.0128 | 0.9463 | |
|
|
| 3.2 | 2000 | 0.0112 | 0.0134 | 0.9502 | |
|
|
| 4.0 | 2500 | 0.01 | 0.0125 | 0.9513 | |
|
|
| 4.8 | 3000 | 0.0097 | 0.0132 | 0.9523 | |
|
|
|
|
|
|
|
|
### Framework Versions |
|
|
- Python: 3.11.11 |
|
|
- Sentence Transformers: 3.3.1 |
|
|
- Transformers: 4.47.1 |
|
|
- PyTorch: 2.5.1+cu124 |
|
|
- Accelerate: 1.2.1 |
|
|
- Datasets: 3.2.0 |
|
|
- Tokenizers: 0.21.0 |
|
|
|
|
|
## Citation |
|
|
|
|
|
### BibTeX |
|
|
|
|
|
#### Sentence Transformers |
|
|
```bibtex |
|
|
@inproceedings{reimers-2019-sentence-bert, |
|
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
|
month = "11", |
|
|
year = "2019", |
|
|
publisher = "Association for Computational Linguistics", |
|
|
url = "https://arxiv.org/abs/1908.10084", |
|
|
} |
|
|
``` |
|
|
|
|
|
#### ContrastiveLoss |
|
|
```bibtex |
|
|
@inproceedings{hadsell2006dimensionality, |
|
|
author={Hadsell, R. and Chopra, S. and LeCun, Y.}, |
|
|
booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)}, |
|
|
title={Dimensionality Reduction by Learning an Invariant Mapping}, |
|
|
year={2006}, |
|
|
volume={2}, |
|
|
number={}, |
|
|
pages={1735-1742}, |
|
|
doi={10.1109/CVPR.2006.100} |
|
|
} |
|
|
``` |
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