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
File size: 609 Bytes
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"bos_token": "<s>",
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"__type": "AddedToken",
"content": "<mask>",
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},
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"model_max_length": 512,
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"pad_token": "<pad>",
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"stride": 0,
"tokenizer_class": "XLMRobertaTokenizer",
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"truncation_strategy": "longest_first",
"unk_token": "<unk>"
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