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--- |
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license: apache-2.0 |
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tags: |
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- sentence-transformers |
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- molecular-similarity |
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- feature-extraction |
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- dense |
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- generated_from_trainer |
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- loss:Matryoshka2dLoss |
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- loss:MatryoshkaLoss |
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- loss:TanimotoSentLoss |
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base_model: Derify/ChemBERTa-druglike |
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widget: |
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- source_sentence: CC1CCc2c(N)nc(C3CCCC3)n2C1 |
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sentences: |
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- CC1CCc2c(N)nc(OC3CC3)n2C1 |
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- CN1CC[NH+](C[C@H](O)C2CC2)C2(CCCCC2)C1 |
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- Cc1c(F)cc(CNCC2CCC(C3CCC(C)CO3)CO2)cc1F |
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- source_sentence: CC(CCCO)NC(=O)CNc1ccccc1 |
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sentences: |
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- CC(CCCO)N[C@H]1CCCN(Nc2ccccc2)[C@H]1C |
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- Cc1ccc(OC2=NCCO2)nc1 |
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- Cc1ccccc1C#Cc1ccccc1N(O)c1ccccc1 |
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- source_sentence: CCCCCCCc1ccc(CC=N[NH+]=C(N)N)cc1 |
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sentences: |
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- COCC1(N2CCN(C)CC2)CCC[NH+]1Cc1cnc(N(C)C)nc1 |
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- Cc1ccc(N=C(c2ccccc2)c2ccc(-n3ccnn3)cc2)cc1 |
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- CCCCCCCc1cncc(CC=N[NH+]=C(N)N)c1 |
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- source_sentence: CC(=CCCS(=O)(=O)[O-])C(=O)OCCCS(=O)(=O)[O-] |
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sentences: |
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- CC(=CCCS(=O)(=O)[O-])C(=O)OCCCS(=O)(=O)[O-] |
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- CCCCCOc1ccc(NC(=S)NC=O)cc1 |
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- CCC(=O)N1CCCC(NC(=O)c2ccc(S(=O)(=O)N(C)C)cc2)C1 |
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- source_sentence: Clc1nccc(C#CCCc2nc3ccccc3o2)n1 |
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sentences: |
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- O=Cc1nc2ccccc2o1 |
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- >- |
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O=C([O-])COc1ccc(CCCS(=O)(=O)c2ccc(Cl)cc2)cc1NC(=O)c1cccc(C=Cc2nc3ccccc3s2)c1 |
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- O[C@H]1CN(C(Cc2ccccc2)c2ccccc2)C[C@@H]1Cc1cnc[nH]1 |
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datasets: |
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- Derify/pubchem_10m_genmol_similarity |
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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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- spearman |
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model-index: |
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- name: 'ChemMRL: SMILES Matryoshka Representation Learning Embedding Transformer' |
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results: |
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- task: |
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type: semantic-similarity |
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name: Semantic Similarity |
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dataset: |
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name: pubchem 10m genmol similarity |
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type: pubchem_10m_genmol_similarity |
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metrics: |
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- type: spearman |
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value: 0.9932120589500998 |
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name: Spearman |
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new_version: Derify/ChemMRL |
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--- |
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# ChemMRL: SMILES Matryoshka Representation Learning Embedding Transformer |
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This is a [Chem-MRL](https://github.com/emapco/chem-mrl) ([sentence-transformers](https://www.SBERT.net)) model finetuned from [Derify/ChemBERTa-druglike](https://huggingface.co/Derify/ChemBERTa-druglike) on the [pubchem_10m_genmol_similarity](https://huggingface.co/datasets/Derify/pubchem_10m_genmol_similarity) dataset. It maps SMILES to a 1024-dimensional dense vector space and can be used for molecular similarity, semantic search, database indexing, molecular classification, clustering, and more. |
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## Model Details |
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### Model Description |
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- **Model Type:** ChemMRL (Sentence Transformer) |
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- **Base model:** [Derify/ChemBERTa-druglike](https://huggingface.co/Derify/ChemBERTa-druglike) <!-- at revision 5e76559157fde4f1aead643d9e1d402289f522af --> |
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- **Maximum Sequence Length:** 128 tokens |
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- **Output Dimensionality:** 1024 dimensions |
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- **Similarity Function:** Tanimoto |
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- **Training Dataset:** |
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- [pubchem_10m_genmol_similarity](https://huggingface.co/datasets/Derify/pubchem_10m_genmol_similarity) |
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- **License:** apache-2.0 |
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### Model Sources |
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- **Repository:** [Chem-MRL on GitHub](https://github.com/emapco/chem-mrl) |
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- **Demo App Repository:** [Chem-MRL-demo on GitHub](https://github.com/emapco/chem-mrl-demo) |
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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': 128, 'do_lower_case': False, 'architecture': 'RobertaModel'}) |
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(1): Pooling({'word_embedding_dimension': 1024, '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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(2): Normalize() |
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) |
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``` |
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## Usage |
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### Direct Usage (Chem-MRL) |
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First install the Chem-MRL library: |
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```bash |
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pip install -U chem-mrl>=0.7.3 |
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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 chem_mrl import ChemMRL |
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# Download from the 🤗 Hub |
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model = ChemMRL("Derify/ChemMRL-beta") |
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# Run inference |
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sentences = [ |
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"Clc1nccc(C#CCCc2nc3ccccc3o2)n1", |
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"O=Cc1nc2ccccc2o1", |
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"O[C@H]1CN(C(Cc2ccccc2)c2ccccc2)C[C@@H]1Cc1cnc[nH]1", |
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] |
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embeddings = model.backbone.encode(sentences) |
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print(embeddings.shape) |
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# [3, 1024] |
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# Get the similarity scores for the embeddings |
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similarities = model.backbone.similarity(embeddings, embeddings) |
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print(similarities) |
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# tensor([[1.0000, 0.3200, 0.1209], |
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# [0.3200, 1.0000, 0.0950], |
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# [0.1209, 0.0950, 1.0000]]) |
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# Load the model with half precision |
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model = ChemMRL("Derify/ChemMRL-beta", use_half_precision=True) |
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sentences = [ |
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"Clc1nccc(C#CCCc2nc3ccccc3o2)n1", |
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"O=Cc1nc2ccccc2o1", |
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"O[C@H]1CN(C(Cc2ccccc2)c2ccccc2)C[C@@H]1Cc1cnc[nH]1", |
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] |
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embeddings = model.embed(sentences) # Use the embed method for half precision |
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print(embeddings.shape) |
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# [3, 1024] |
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``` |
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## Evaluation |
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### Metrics |
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#### Semantic Similarity |
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* Dataset: `pubchem_10m_genmol_similarity` |
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* Evaluated with <code>chem_mrl.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator</code> with these parameters: |
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```json |
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{ |
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"precision": "float32" |
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} |
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``` |
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| Split | Metric | Value | |
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| :------------- | :----------- | :----------- | |
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| **validation** | **spearman** | **0.993212** | |
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| **test** | **spearman** | **0.993243** | |
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## Training Details |
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### Training Dataset |
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#### pubchem_10m_genmol_similarity |
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* Dataset: [pubchem_10m_genmol_similarity](https://huggingface.co/datasets/Derify/pubchem_10m_genmol_similarity) at [f68d779](https://huggingface.co/datasets/Derify/pubchem_10m_genmol_similarity/tree/f68d779a6284578132a3922655f6b1f74c576642) |
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* Size: 19,692,766 training samples |
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* Columns: <code>smiles_a</code>, <code>smiles_b</code>, and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | smiles_a | smiles_b | label | |
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| :------ | :---------------------------------------------------------------------------------- | :---------------------------------------------------------------------------------- | :-------------------------------------------------------------- | |
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| type | string | string | float | |
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| details | <ul><li>min: 17 tokens</li><li>mean: 39.66 tokens</li><li>max: 119 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 38.29 tokens</li><li>max: 115 tokens</li></ul> | <ul><li>min: 0.02</li><li>mean: 0.57</li><li>max: 1.0</li></ul> | | <code>0.7123287916183472</code> | |
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* Loss: [<code>Matryoshka2dLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshka2dloss) with these parameters: |
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<details><summary>Click to expand</summary> |
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```json |
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{ |
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"loss": "TanimotoSentLoss", |
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"n_layers_per_step": -1, |
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"last_layer_weight": 2.0, |
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"prior_layers_weight": 1.0, |
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"kl_div_weight": 0.5, |
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"kl_temperature": 0.3, |
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"matryoshka_dims": [ |
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1024, |
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512, |
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256, |
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128, |
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64, |
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32, |
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16, |
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8 |
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], |
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"matryoshka_weights": [ |
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1, |
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1, |
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1, |
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1, |
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1, |
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1, |
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1, |
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1 |
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], |
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"n_dims_per_step": -1 |
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} |
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``` |
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</details> |
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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`: 64 |
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- `per_device_eval_batch_size`: 128 |
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- `learning_rate`: 8e-06 |
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- `weight_decay`: 6.505130550397454e-06 |
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- `warmup_ratio`: 0.2 |
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- `data_seed`: 42 |
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- `fp16`: True |
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- `tf32`: True |
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- `load_best_model_at_end`: True |
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- `optim`: adamw_apex_fused |
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- `dataloader_pin_memory`: False |
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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`: 64 |
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- `per_device_eval_batch_size`: 128 |
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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`: 8e-06 |
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- `weight_decay`: 6.505130550397454e-06 |
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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`: 3 |
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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`: 0.2 |
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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`: 42 |
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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`: True |
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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`: 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_apex_fused |
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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`: False |
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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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- `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`: 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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- `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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- `router_mapping`: {} |
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- `learning_rate_mapping`: {} |
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</details> |
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### Training Logs |
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<details><summary>Click to expand</summary> |
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| Epoch | Step | Training Loss | pubchem_10m_genmol_similarity_spearman | |
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| :----: | :----: | :-----------: | :------------------------------------: | |
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| 0.0796 | 24500 | 121.4633 | - | |
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| 0.08 | 24616 | - | 0.9739 | |
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| 0.1592 | 49000 | 118.6111 | - | |
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| 0.16 | 49232 | - | 0.9817 | |
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| 0.2389 | 73500 | 117.491 | - | |
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| 0.24 | 73848 | - | 0.9848 | |
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| 0.3185 | 98000 | 116.3786 | - | |
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| 0.32 | 98464 | - | 0.9865 | |
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| 0.3997 | 123000 | 115.9773 | - | |
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| 0.4 | 123080 | - | 0.9873 | |
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| 0.4794 | 147500 | 115.2441 | - | |
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| 0.48 | 147696 | - | 0.9885 | |
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| 0.5590 | 172000 | 114.8674 | - | |
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| 0.56 | 172312 | - | 0.9887 | |
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| 0.6386 | 196500 | 114.6483 | - | |
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| 0.64 | 196928 | - | 0.9892 | |
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| 0.7199 | 221500 | 114.0507 | - | |
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| 0.72 | 221544 | - | 0.9898 | |
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| 0.7995 | 246000 | 113.5606 | - | |
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| 0.8 | 246160 | - | 0.9902 | |
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| 0.8791 | 270500 | 113.2762 | - | |
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| 0.88 | 270776 | - | 0.9907 | |
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| 0.9587 | 295000 | 113.3295 | - | |
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| 0.96 | 295392 | - | 0.9908 | |
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| 1.0400 | 320000 | 112.9253 | - | |
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| 1.04 | 320008 | - | 0.9909 | |
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| 1.1196 | 344500 | 112.584 | - | |
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| 1.12 | 344624 | - | 0.9910 | |
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| 1.1992 | 369000 | 112.616 | - | |
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| 1.2 | 369240 | - | 0.9916 | |
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| 1.2788 | 393500 | 112.4692 | - | |
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| 1.28 | 393856 | - | 0.9914 | |
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| 1.3585 | 418000 | 112.2679 | - | |
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| 1.3600 | 418472 | - | 0.9917 | |
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| 1.4397 | 443000 | 112.1639 | - | |
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| 1.44 | 443088 | - | 0.9919 | |
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| 1.5193 | 467500 | 112.1139 | - | |
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| 1.52 | 467704 | - | 0.9921 | |
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| 1.5990 | 492000 | 111.8096 | - | |
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| 1.6 | 492320 | - | 0.9923 | |
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| 1.6786 | 516500 | 111.8252 | - | |
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| 1.6800 | 516936 | - | 0.9922 | |
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| 1.7598 | 541500 | 111.836 | - | |
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| 1.76 | 541552 | - | 0.9924 | |
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| 1.8395 | 566000 | 111.8471 | - | |
|
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| 1.8400 | 566168 | - | 0.9924 | |
|
|
| 1.9191 | 590500 | 111.7778 | - | |
|
|
| 1.92 | 590784 | - | 0.9925 | |
|
|
| 1.9987 | 615000 | 111.4892 | - | |
|
|
| 2.0 | 615400 | - | 0.9927 | |
|
|
| 2.0799 | 640000 | 111.2659 | - | |
|
|
| 2.08 | 640016 | - | 0.9928 | |
|
|
| 2.1596 | 664500 | 111.3635 | - | |
|
|
| 2.16 | 664632 | - | 0.9927 | |
|
|
| 2.2392 | 689000 | 111.0114 | - | |
|
|
| 2.24 | 689248 | - | 0.9928 | |
|
|
| 2.3188 | 713500 | 111.0559 | - | |
|
|
| 2.32 | 713864 | - | 0.9929 | |
|
|
| 2.3984 | 738000 | 110.5276 | - | |
|
|
| 2.4 | 738480 | - | 0.9929 | |
|
|
| 2.4797 | 763000 | 110.9828 | - | |
|
|
| 2.48 | 763096 | - | 0.9930 | |
|
|
| 2.5593 | 787500 | 110.8404 | - | |
|
|
| 2.56 | 787712 | - | 0.9930 | |
|
|
| 2.6389 | 812000 | 111.1937 | - | |
|
|
| 2.64 | 812328 | - | 0.9931 | |
|
|
| 2.7186 | 836500 | 110.6662 | - | |
|
|
| 2.7200 | 836944 | - | 0.9931 | |
|
|
| 2.7998 | 861500 | 110.7714 | - | |
|
|
| 2.8 | 861560 | - | 0.9932 | |
|
|
| 2.8794 | 886000 | 110.7638 | - | |
|
|
| 2.88 | 886176 | - | 0.9932 | |
|
|
| 2.9591 | 910500 | 110.7021 | - | |
|
|
| 2.96 | 910792 | - | 0.9932 | |
|
|
| 2.9997 | 923000 | 110.6097 | - | |
|
|
</details> |
|
|
|
|
|
### Training Hardware |
|
|
- **On Cloud**: No |
|
|
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090 |
|
|
- **CPU Model**: AMD Ryzen 7 3700X 8-Core Processor |
|
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- **RAM Size**: 62.70 GB |
|
|
|
|
|
### Framework Versions |
|
|
- Python: 3.12.11 |
|
|
- Sentence Transformers: 5.0.0 |
|
|
- Transformers: 4.53.3 |
|
|
- PyTorch: 2.7.1+cu126 |
|
|
- Accelerate: 1.9.0 |
|
|
- Datasets: 3.6.0 |
|
|
- Tokenizers: 0.21.2 |
|
|
|
|
|
## 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", |
|
|
} |
|
|
``` |
|
|
|
|
|
#### Matryoshka2dLoss |
|
|
```bibtex |
|
|
@misc{li20242d, |
|
|
title={2D Matryoshka Sentence Embeddings}, |
|
|
author={Xianming Li and Zongxi Li and Jing Li and Haoran Xie and Qing Li}, |
|
|
year={2024}, |
|
|
eprint={2402.14776}, |
|
|
archivePrefix={arXiv}, |
|
|
primaryClass={cs.CL} |
|
|
} |
|
|
``` |
|
|
|
|
|
#### MatryoshkaLoss |
|
|
```bibtex |
|
|
@misc{kusupati2024matryoshka, |
|
|
title={Matryoshka Representation Learning}, |
|
|
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, |
|
|
year={2024}, |
|
|
eprint={2205.13147}, |
|
|
archivePrefix={arXiv}, |
|
|
primaryClass={cs.LG} |
|
|
} |
|
|
``` |
|
|
|
|
|
#### CoSENTLoss |
|
|
```bibtex |
|
|
@online{kexuefm-8847, |
|
|
title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT}, |
|
|
author={Su Jianlin}, |
|
|
year={2022}, |
|
|
month={Jan}, |
|
|
url={https://kexue.fm/archives/8847}, |
|
|
} |
|
|
``` |
|
|
|
|
|
#### TanimotoSentLoss |
|
|
```bibtex |
|
|
@online{cortes-2025-tanimotosentloss, |
|
|
title={TanimotoSentLoss: Tanimoto Loss for SMILES Embeddings}, |
|
|
author={Emmanuel Cortes}, |
|
|
year={2025}, |
|
|
month={Jan}, |
|
|
url={https://github.com/emapco/chem-mrl}, |
|
|
} |
|
|
``` |
|
|
|
|
|
## Model Card Authors |
|
|
|
|
|
[@eacortes](https://huggingface.co/eacortes) |
|
|
|
|
|
## Model Card Contact |
|
|
|
|
|
Manny Cortes ([email protected]) |
|
|
|
