Feature Extraction
sentence-transformers
Safetensors
Transformers
English
mistral
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use Salesforce/SFR-Embedding-Mistral with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Salesforce/SFR-Embedding-Mistral with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Salesforce/SFR-Embedding-Mistral") 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 Salesforce/SFR-Embedding-Mistral with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Salesforce/SFR-Embedding-Mistral")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Salesforce/SFR-Embedding-Mistral") model = AutoModel.from_pretrained("Salesforce/SFR-Embedding-Mistral") - Notebooks
- Google Colab
- Kaggle
| { | |
| "alpha_pattern": {}, | |
| "auto_mapping": null, | |
| "base_model_name_or_path": "intfloat/e5-mistral-7b-instruct", | |
| "bias": "none", | |
| "fan_in_fan_out": false, | |
| "inference_mode": true, | |
| "init_lora_weights": true, | |
| "layers_pattern": null, | |
| "layers_to_transform": null, | |
| "loftq_config": {}, | |
| "lora_alpha": 32, | |
| "lora_dropout": 0.1, | |
| "megatron_config": null, | |
| "megatron_core": "megatron.core", | |
| "modules_to_save": null, | |
| "peft_type": "LORA", | |
| "r": 8, | |
| "rank_pattern": {}, | |
| "revision": null, | |
| "target_modules": [ | |
| "v_proj", | |
| "o_proj", | |
| "k_proj", | |
| "up_proj", | |
| "q_proj", | |
| "gate_proj", | |
| "down_proj" | |
| ], | |
| "task_type": "FEATURE_EXTRACTION" | |
| } |