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
| { | |
| "__version__": { | |
| "sentence_transformers": "2.7.0", | |
| "transformers": "4.39.3", | |
| "pytorch": "2.1.0+cu121" | |
| }, | |
| "prompts": { | |
| "web_search_query": "Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery: ", | |
| "sts_query": "Instruct: Retrieve semantically similar text.\nQuery: ", | |
| "summarization_query": "Instruct: Given a news summary, retrieve other semantically similar summaries\nQuery: ", | |
| "bitext_query": "Instruct: Retrieve parallel sentences.\nQuery: " | |
| }, | |
| "default_prompt_name": null | |
| } |