Sentence Similarity
sentence-transformers
Safetensors
English
ESMplusplus
protein
esm-c
contrastive-learning
protein-embeddings
biology
custom_code
Instructions to use GrimSqueaker/ProtSent-V2-ESMC-300M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use GrimSqueaker/ProtSent-V2-ESMC-300M with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("GrimSqueaker/ProtSent-V2-ESMC-300M", trust_remote_code=True) 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
| { | |
| "backend": "tokenizers", | |
| "cls_token": "<cls>", | |
| "eos_token": "<eos>", | |
| "extra_special_tokens": [ | |
| "|" | |
| ], | |
| "is_local": true, | |
| "local_files_only": true, | |
| "mask_token": "<mask>", | |
| "model_max_length": 512, | |
| "pad_token": "<pad>", | |
| "tokenizer_class": "TokenizersBackend", | |
| "unk_token": "<unk>" | |
| } | |