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
How to evaluate the differences in response quality between the base Mistral-7B-v0.1 model and the fine-tuned e5-mistral-7b-instruct model
#48
by emilylilil - opened
Hi,
According to the documentation, e5-mistral-7b-instruct was fine-tuned with LoRA on multilingual datasets. While it has some multilingual capabilities, it is still mainly recommended for English tasks, whereas the base Mistral-7B-v0.1 is primarily trained on English data.
I have the following question:
What are the key differences between Mistral-7B-v0.1 and e5-mistral-7b-instruct and how to compare it?
I’d appreciate any insights from the community on these points. Thanks in advance!