Sentence Similarity
sentence-transformers
Safetensors
roberta
molecular-similarity
feature-extraction
dense
Generated from Trainer
loss:Matryoshka2dLoss
loss:MatryoshkaLoss
loss:TanimotoSentLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Derify/ChemMRL-beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Derify/ChemMRL-beta with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Derify/ChemMRL-beta") sentences = [ "CC1CCc2c(N)nc(C3CCCC3)n2C1", "CC1CCc2c(N)nc(OC3CC3)n2C1", "CN1CC[NH+](C[C@H](O)C2CC2)C2(CCCCC2)C1", "Cc1c(F)cc(CNCC2CCC(C3CCC(C)CO3)CO2)cc1F" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "RobertaModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 0, | |
| "classifier_dropout": null, | |
| "eos_token_id": 2, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 1024, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4096, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 130, | |
| "model_type": "roberta", | |
| "num_attention_heads": 8, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 1, | |
| "position_embedding_type": "absolute", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.53.3", | |
| "type_vocab_size": 1, | |
| "use_cache": false, | |
| "vocab_size": 581 | |
| } | |