Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
loss:MatryoshkaLoss
loss:CosineSimilarityLoss
chemistry
biology
drug-discovery
herbal
coconutdb
chembl34
selfies
drugs
molecules
compounds
Eval Results (legacy)
text-embeddings-inference
Instructions to use gbyuvd/chemembed-chemselfies with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use gbyuvd/chemembed-chemselfies with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("gbyuvd/chemembed-chemselfies") sentences = [ "[N] [C] [=N] [C] [=N] [C] [=C] [Ring1] [=Branch1] [S] [C] [=C] [C] [Branch1] [=Branch2] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [=C] [C] [=C] [Ring1] [N] [Ring1] [#C]", "[C] [C] [C] [C] [C@H1] [Branch2] [#Branch2] [Branch2] [N] [C] [=Branch1] [C] [=O] [C@@H1] [C] [C] [C] [C] [N] [C] [=Branch1] [C] [=O] [C] [C] [C@H1] [Branch2] [=Branch1] [S] [N] [C] [=Branch1] [C] [=O] [C@H1] [Branch1] [#Branch2] [C] [C] [C] [N] [=C] [Branch1] [C] [N] [N] [N] [C] [=Branch1] [C] [=O] [C@H1] [Branch1] [#Branch1] [C] [C] [Branch1] [C] [C] [C] [N] [C] [=Branch1] [C] [=O] [C@H1] [Branch1] [#Branch1] [C] [C] [Branch1] [C] [C] [C] [N] [C] [=Branch1] [C] [=O] [C@H1] [Branch1] [=Branch2] [C] [C] [=C] [NH1] [C] [=N] [Ring1] [Branch1] [N] [C] [=Branch1] [C] [=O] [C@H1] [Branch1] [C] [N] [C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=Branch1] [C] [=O] [N] [C@@H1] [Branch1] [=Branch1] [C] [Branch1] [C] [C] [C] [C] [=Branch1] [C] [=O] [N] [C@H1] [Branch1] [#Branch1] [C] [C] [Branch1] [C] [C] [C] [C] [=Branch1] [C] [=O] [N] [Ring2] [=Branch1] [=C] [C] [=Branch1] [C] [=O] [N] [C@@H1] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [N] [C@@H1] [Branch1] [#Branch2] [C] [C] [C] [N] [=C] [Branch1] [C] [N] [N] [C] [=Branch1] [C] [=O] [N] [C@@H1] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [N] [C@@H1] [Branch1] [Branch2] [C] [C] [C] [=Branch1] [C] [=O] [O] [C] [=Branch1] [C] [=O] [N] [C@@H1] [Branch1] [Branch2] [C] [C] [C] [Branch1] [C] [N] [=O] [C] [=Branch1] [C] [=O] [N] [C@@H1] [Branch1] [#Branch1] [C] [C] [Branch1] [C] [C] [C] [C] [=Branch1] [C] [=O] [N] [C@@H1] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [N] [C@@H1] [Branch1] [Branch2] [C] [C] [C] [Branch1] [C] [N] [=O] [C] [=Branch1] [C] [=O] [N] [C@@H1] [Branch1] [Branch2] [C] [C] [C] [Branch1] [C] [N] [=O] [C] [=Branch1] [C] [=O] [N] [C@@H1] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [N] [C@@H1] [Branch1] [=Branch2] [C] [C] [=C] [NH1] [C] [=N] [Ring1] [Branch1] [C] [=Branch1] [C] [=O] [N] [C@@H1] [Branch1] [Ring1] [C] [O] [C] [=Branch1] [C] [=O] [N] [C@@H1] [Branch1] [#Branch1] [C] [C] [Branch1] [C] [N] [=O] [C] [=Branch1] [C] [=O] [N] [C@@H1] [Branch1] [#Branch2] [C] [C] [C] [N] [=C] [Branch1] [C] [N] [N] [C] [=Branch1] [C] [=O] [N] [C@@H1] [Branch1] [=Branch1] [C] [C] [C] [C] [N] [C] [=Branch1] [C] [=O] [N] [C@@H1] [Branch1] [#Branch1] [C] [C] [Branch1] [C] [C] [C] [C] [=Branch1] [C] [=O] [N] [C@@H1] [Branch1] [Branch1] [C] [C] [C] [C] [C] [=Branch1] [C] [=O] [N] [C@@H1] [Branch1] [Branch2] [C] [C] [C] [=Branch1] [C] [=O] [O] [C] [=Branch1] [C] [=O] [N] [C@H1] [Branch2] [Ring1] [Ring2] [C] [=Branch1] [C] [=O] [N] [C@H1] [Branch1] [=Branch1] [C] [Branch1] [C] [N] [=O] [C@@H1] [Branch1] [C] [C] [C] [C] [C@@H1] [Branch1] [C] [C] [C] [C]", "[C] [C] [=Branch1] [C] [=O] [N] [C@H1] [C@H1] [Branch2] [Ring2] [#Branch2] [O] [C@H1] [C@@H1] [Branch1] [C] [O] [C@@H1] [Branch1] [Ring1] [C] [O] [O] [C@@H1] [Branch2] [Ring1] [Branch1] [O] [C@H1] [C@H1] [Branch1] [C] [O] [C@@H1] [Branch1] [C] [O] [C@H1] [Branch1] [C] [O] [O] [C@@H1] [Ring1] [=Branch2] [C] [O] [C@@H1] [Ring2] [Ring1] [Branch1] [O] [O] [C@H1] [Branch1] [Ring1] [C] [O] [C@@H1] [Branch1] [C] [O] [C@@H1] [Ring2] [Ring1] [S] [O] [C@@H1] [O] [C@H1] [Branch1] [Ring1] [C] [O] [C@H1] [Branch1] [C] [O] [C@H1] [Branch1] [C] [O] [C@H1] [Ring1] [#Branch2] [O]", "[C] [C] [=C] [C] [=C] [C] [Branch2] [Ring1] [Ring1] [N] [C] [=Branch1] [C] [=O] [C] [O] [C] [=C] [C] [=C] [C] [Branch1] [C] [C] [=C] [Ring1] [#Branch1] [=C] [Ring2] [Ring1] [C]" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "./sf/np", | |
| "architectures": [ | |
| "BertModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 320, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 1280, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 4, | |
| "num_hidden_layers": 8, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.42.4", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 3095 | |
| } | |