Instructions to use yosshstd/ProTrek_650M_UniRef50_protein_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use yosshstd/ProTrek_650M_UniRef50_protein_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("yosshstd/ProTrek_650M_UniRef50_protein_encoder") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download config.json from yosshstd/ProTrek_650M_UniRef50_protein_encoder: direct link, hf CLI and curl.
- Browser
- Download file 713 Bytes
-
https://huggingface.co/yosshstd/ProTrek_650M_UniRef50_protein_encoder/resolve/main/config.json
- Command line
-
hf download hf://yosshstd/ProTrek_650M_UniRef50_protein_encoder/config.json
-
curl -L -o config.json https://huggingface.co/yosshstd/ProTrek_650M_UniRef50_protein_encoder/resolve/main/config.json
713 Bytes
| { | |
| "architectures": [ | |
| "EsmModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.0, | |
| "classifier_dropout": null, | |
| "emb_layer_norm_before": false, | |
| "esmfold_config": null, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.0, | |
| "hidden_size": 1280, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 5120, | |
| "is_folding_model": false, | |
| "layer_norm_eps": 1e-05, | |
| "mask_token_id": 32, | |
| "max_position_embeddings": 1026, | |
| "model_type": "esm", | |
| "num_attention_heads": 20, | |
| "num_hidden_layers": 33, | |
| "pad_token_id": 1, | |
| "position_embedding_type": "rotary", | |
| "token_dropout": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.53.2", | |
| "use_cache": true, | |
| "vocab_list": null, | |
| "vocab_size": 33 | |
| } | |