Sentence Similarity
sentence-transformers
PyTorch
Transformers
English
bert
feature-extraction
text-embeddings-inference
Instructions to use OpenMOSS-Team/claif-scaled-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-scaled-bert-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("OpenMOSS-Team/claif-scaled-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-scaled-bert-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("OpenMOSS-Team/claif-scaled-bert-base") model = AutoModel.from_pretrained("OpenMOSS-Team/claif-scaled-bert-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7fa8e7d0f894e7863b13204c9690585ee6d078c3233c9a5dd8281556933cacc3
- Size of remote file:
- 438 MB
- SHA256:
- 72bad77459def77c3b26dab47ec7c60d8c87051fb9079e3d8f549744d43cd119
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