Feature Extraction
sentence-transformers
PyTorch
Safetensors
Transformers
English
mistral
mteb
Eval Results (legacy)
Eval Results
text-embeddings-inference
Instructions to use intfloat/e5-mistral-7b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use intfloat/e5-mistral-7b-instruct with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("intfloat/e5-mistral-7b-instruct") 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 intfloat/e5-mistral-7b-instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="intfloat/e5-mistral-7b-instruct")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("intfloat/e5-mistral-7b-instruct") model = AutoModel.from_pretrained("intfloat/e5-mistral-7b-instruct") - Inference
- Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences.
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## Limitations
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Using this model for inputs longer than 4096 tokens is not recommended.
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## Citation
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If you find our paper or models helpful, please consider cite as follows:
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```bibtex
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@article{wang2023improving,
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title={Improving Text Embeddings with Large Language Models},
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author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu},
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journal={arXiv preprint arXiv:2401.00368},
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year={2023}
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}
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@article{wang2022text,
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title={Text Embeddings by Weakly-Supervised Contrastive Pre-training},
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author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu},
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journal={arXiv preprint arXiv:2212.03533},
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year={2022}
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}
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```
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## Limitations
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Using this model for inputs longer than 4096 tokens is not recommended.
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