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")# 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") - Notebooks
- Google Colab
- Kaggle
how is this different from the main e5-large-v2?
#1
by vasilee - opened
I tried with both and they both generate the same embeddings
It contains a handler.py so it can be used in a huggingface inference endpoint. Planning to make a PR to upstream at some point, and should add that to the readme.
hansihe changed discussion status to closed
yes, please update as this is confusing,
TheBloke is also doing this kind of stuff but he is always putting a link in the readme to the original with clarifications about what is different,
also not sure it is a good idea to submit it to the scoreboard if it is the same model and exactly the same performance