Sentence Similarity
sentence-transformers
Safetensors
Transformers
new
feature-extraction
mteb
multilingual
text-embeddings-inference
custom_code
Eval Results (legacy)
Instructions to use Alibaba-NLP/gte-multilingual-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Alibaba-NLP/gte-multilingual-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Alibaba-NLP/gte-multilingual-base", trust_remote_code=True) sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use Alibaba-NLP/gte-multilingual-base with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Alibaba-NLP/gte-multilingual-base", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md (#16)
Browse files- Update README.md (89963044ae0bbb80ba66310c3faa226cfaa91184)
Co-authored-by: jackchris121 <jackchris121@users.noreply.huggingface.co>
README.md
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### Use with sentence-transformers
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```python
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# Requires
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from sentence_transformers import SentenceTransformer
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### Use with sentence-transformers
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```python
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# Requires sentence-transformers>=3.0.0
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from sentence_transformers import SentenceTransformer
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