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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add link to technical report
Browse files
README.md
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## E5-mistral-7b-instruct
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Some highlights for preview:
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* This model is only fine-tuned for less than 1000 steps, no contrastive pre-training is used.
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* For a large part of training data, the query, positive documents and negative documents are all generated by LLMs.
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This model has 32 layers and the embedding size is 4096.
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## E5-mistral-7b-instruct
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[Improving Text Embeddings with Large Language Models](https://arxiv.org/pdf/2401.00368.pdf). Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024
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This model has 32 layers and the embedding size is 4096.
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