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:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Alibaba-NLP/gte-multilingual-base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -4795,6 +4795,8 @@ In addition to the open-source [GTE](https://huggingface.co/collections/Alibaba-
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Note that the models behind the commercial APIs are not entirely identical to the open-source models.
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## Citation
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```
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@misc{zhang2024mgte,
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title={mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval},
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Note that the models behind the commercial APIs are not entirely identical to the open-source models.
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## Citation
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If you find our paper or models helpful, please consider cite:
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```
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@misc{zhang2024mgte,
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title={mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval},
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