Sentence Similarity
sentence-transformers
PyTorch
Transformers
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use kaiserrr/Bilingual-BioSimCSE-BioLinkBERT-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use kaiserrr/Bilingual-BioSimCSE-BioLinkBERT-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("kaiserrr/Bilingual-BioSimCSE-BioLinkBERT-base") 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 kaiserrr/Bilingual-BioSimCSE-BioLinkBERT-base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("kaiserrr/Bilingual-BioSimCSE-BioLinkBERT-base") model = AutoModel.from_pretrained("kaiserrr/Bilingual-BioSimCSE-BioLinkBERT-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from kaiserrr/Bilingual-BioSimCSE-BioLinkBERT-base: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/kaiserrr/Bilingual-BioSimCSE-BioLinkBERT-base/resolve/main/tokenizer.json
- Command line
-
hf download hf://kaiserrr/Bilingual-BioSimCSE-BioLinkBERT-base/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/kaiserrr/Bilingual-BioSimCSE-BioLinkBERT-base/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- fe584e09808b408472e9556eb0d8db4b797690702ab6f43ad18e59f5b5b2cd10
- Size of remote file:
- 17.1 MB
- SHA256:
- b60b6b43406a48bf3638526314f3d232d97058bc93472ff2de930d43686fa441
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