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:
# 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
- Xet hash:
- 8e87293f61fe3242186f5a63175c75cba3a67e5a71db3158c4984e176a9fed03
- Size of remote file:
- 1.11 GB
- SHA256:
- 45c271b44929a42a8f20d1d05fb08acfd233f8d2932306a1493daf67e01aa654
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