Sentence Similarity
sentence-transformers
PyTorch
Transformers
English
bert
feature-extraction
text-embeddings-inference
Instructions to use OpenMOSS-Team/claif-bert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use OpenMOSS-Team/claif-bert-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("OpenMOSS-Team/claif-bert-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 OpenMOSS-Team/claif-bert-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("OpenMOSS-Team/claif-bert-base") model = AutoModel.from_pretrained("OpenMOSS-Team/claif-bert-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5ff39a5d0b25d9ec371912954abc365a85b1aac29ccaf03a3d7074ae0deaf4da
- Size of remote file:
- 438 MB
- SHA256:
- a7fdadcf304a261fbb2b3cd4d2b13a7c7979322a07c62431de877622c6480b37
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