Sentence Similarity
sentence-transformers
Safetensors
Transformers
bert
feature-extraction
biology
protein language model
text-embeddings-inference
Instructions to use monsoon-nlp/protein-matryoshka-embeddings with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use monsoon-nlp/protein-matryoshka-embeddings with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("monsoon-nlp/protein-matryoshka-embeddings") 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] - Transformers
How to use monsoon-nlp/protein-matryoshka-embeddings with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("monsoon-nlp/protein-matryoshka-embeddings") model = AutoModel.from_pretrained("monsoon-nlp/protein-matryoshka-embeddings", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from monsoon-nlp/protein-matryoshka-embeddings: direct link, hf CLI and curl.
- Browser
- Download file 1.68 GB
-
https://huggingface.co/monsoon-nlp/protein-matryoshka-embeddings/resolve/main/model.safetensors
- Command line
-
hf download hf://monsoon-nlp/protein-matryoshka-embeddings/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/monsoon-nlp/protein-matryoshka-embeddings/resolve/main/model.safetensors
1.68 GB
- Xet hash:
- 664d47a2ddc80479d115e3b16722f8ea7f984e177c2db9f6edaa3eb750773eda
- Size of remote file:
- 1.68 GB
- SHA256:
- 99cb4614454708365e49b7595159aabf060ce6c0de80bd4011c35e8fc0e4e9b5
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