Feature Extraction
sentence-transformers
Safetensors
Transformers
English
mistral
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use Salesforce/SFR-Embedding-2_R with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Salesforce/SFR-Embedding-2_R with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Salesforce/SFR-Embedding-2_R") 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-2_R with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Salesforce/SFR-Embedding-2_R")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Salesforce/SFR-Embedding-2_R") model = AutoModel.from_pretrained("Salesforce/SFR-Embedding-2_R", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Update README.md
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Usage: Infinity
Verified signatureWorks: ``` docker run --gpus all -v $PWD/data:/app/.cache -e HF_TOKEN=$HF_TOKEN -p "7999":"7997" michaelf34/infinity:0.0.68 v2 --model-id Salesforce/SFR-Embedding-2_R --revision "91762139d94ed4371a9fa31db5551272e0b83818" --dtype bfloat16 --batch-size 4 --device cuda --engine torch --port 7997 --no-bettertransformer INFO: Started server process [1] INFO: Waiting for application startup. INFO 2024-11-13 00:05:11,159 infinity_emb INFO: infinity_server.py:89 Creating 1engines: engines=['Salesforce/SFR-Embedding-2_R'] INFO 2024-11-13 00:05:11,163 infinity_emb INFO: Anonymized telemetry.py:30 telemetry can be disabled via environment variable `DO_NOT_TRACK=1`. INFO 2024-11-13 00:05:11,171 infinity_emb INFO: select_model.py:64 model=`Salesforce/SFR-Embe…4f86794
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Add proper Sentence Transformers integration (#1)
Verified signature- Add ST-specific configuration files with model.save() (7260df65f5d871b2743995da4cc2342b8f954844) - Also add a transformers tag (c58c14f76620c82c5e8e307c91e8edda2eda2a72) - Following SFR-Embedding-Mistral, set max seq length to 4096 (6cf3bfedf3c5c2d24b7730664985cdc11ffbb154)3388895
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sfr-embedding-v2
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