Feature Extraction
Transformers
PyTorch
Safetensors
English
bert
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use fresha/e5-large-v2-endpoint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fresha/e5-large-v2-endpoint with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="fresha/e5-large-v2-endpoint")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("fresha/e5-large-v2-endpoint") model = AutoModel.from_pretrained("fresha/e5-large-v2-endpoint", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
fix norm
Hans Elias Jce64906 -
normalize embedding vector
Hans Elias J0996185 -
return embeddings list directly
Hans Elias Je66ef1e -
fix return types
Hans Elias Jee4ad34 -
convert embeddings to list before returning
Hans Elias Jfd7e3f2 -
add inference endpoints handler.py
Hans Elias J59c3fb3