Instructions to use Jun421/MVP-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jun421/MVP-base with Transformers:
# Load model directly from transformers import AutoTokenizer, FiDT5 tokenizer = AutoTokenizer.from_pretrained("Jun421/MVP-base") model = FiDT5.from_pretrained("Jun421/MVP-base") - Notebooks
- Google Colab
- Kaggle
Add model card with pipeline tag, license, library name, and usage instructions
#1
by nielsr HF Staff - opened
This PR adds a model card for the MVP model.
It includes:
- A link to the paper: Multi-view-guided Passage Reranking with Large Language Models
- A link to the GitHub repository: https://github.com/bulbna/MVP
- The
pipeline_tag: text-ranking, ensuring discoverability on the Hugging Face Hub. - The
library_name: transformers, indicating compatibility with the Hugging Face Transformers library. - The
license: apache-2.0. - A setup and run instruction from the Github README.