Sentence Similarity
sentence-transformers
PyTorch
ONNX
Safetensors
OpenVINO
English
bert
mteb
Sentence Transformers
Eval Results (legacy)
text-embeddings-inference
Instructions to use thenlper/gte-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use thenlper/gte-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("thenlper/gte-large") 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] - Inference
- Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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If you find our paper or models helpful, please consider citing them as follows:
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@misc{li2023general,
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title={Towards General Text Embeddings with Multi-stage Contrastive Learning},
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author={Zehan Li and Xin Zhang and Yanzhao Zhang and Dingkun Long and Pengjun Xie and Meishan Zhang},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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If you find our paper or models helpful, please consider citing them as follows:
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```
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@misc{li2023general,
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title={Towards General Text Embeddings with Multi-stage Contrastive Learning},
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author={Zehan Li and Xin Zhang and Yanzhao Zhang and Dingkun Long and Pengjun Xie and Meishan Zhang},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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