Instructions to use rlhn/e5-base-remove-400K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rlhn/e5-base-remove-400K with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="rlhn/e5-base-remove-400K")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("rlhn/e5-base-remove-400K") model = AutoModel.from_pretrained("rlhn/e5-base-remove-400K") - Notebooks
- Google Colab
- Kaggle
Update model card
#1
by nielsr HF Staff - opened
This PR updates the model card to provide a short description of the model, a link to the paper Fixing Data That Hurts Performance: Cascading LLMs to Relabel Hard Negatives for Robust Information Retrieval, and a link to the Github repository.