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
File size: 310 Bytes
0b53d36 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | {
"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>"
}
|