Sentence Similarity
sentence-transformers
Safetensors
modchembert
cheminformatics
smiles
molecular-similarity
feature-extraction
dense
Generated from Trainer
dataset_size:19381001
loss:Matryoshka2dLoss
loss:MatryoshkaLoss
loss:TanimotoSentLoss
custom_code
Eval Results (legacy)
Instructions to use Derify/ChemMRL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Derify/ChemMRL with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Derify/ChemMRL", trust_remote_code=True) sentences = [ "COC(=O)c1sc(-c2ccc(C)cc2)c2c1NC(=O)C2(c1ccccc1)c1ccccc1", "COC(=O)c1sc(Nc2ccc(Br)cn2)c2c1NC(=O)C2(c1ccccc1)c1ccccc1", "CC[NH+]1CCOC(C(NN)c2ccccc2Br)C1", "CC([NH2+]C(C)c1ccccc1)C(=O)P(C)C(C)(C)C" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "added_tokens_decoder": { | |
| "0": { | |
| "content": "[CLS]", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "1": { | |
| "content": "[SEP]", | |
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| "single_word": false, | |
| "special": true | |
| }, | |
| "2": { | |
| "content": "[PAD]", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "3": { | |
| "content": "[MASK]", | |
| "lstrip": true, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "2361": { | |
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| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| } | |
| }, | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "[CLS]", | |
| "extra_special_tokens": {}, | |
| "mask_token": "[MASK]", | |
| "model_input_names": [ | |
| "input_ids", | |
| "attention_mask" | |
| ], | |
| "model_max_length": 512, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "tokenizer_class": "PreTrainedTokenizerFast", | |
| "unk_token": "[UNK]" | |
| } | |