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, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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If you find our paper or models helpful, please consider cite:
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2407.19669},
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```
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If you find our paper or models helpful, please consider cite:
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```
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@inproceedings{zhang2024mgte,
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title={mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval},
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author={Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Wen and Dai, Ziqi and Tang, Jialong and Lin, Huan and Yang, Baosong and Xie, Pengjun and Huang, Fei and others},
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booktitle={Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track},
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pages={1393--1412},
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year={2024}
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}
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```
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