Instructions to use Jun421/MVP-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jun421/MVP-3b with Transformers:
# Load model directly from transformers import AutoTokenizer, FiDT5 tokenizer = AutoTokenizer.from_pretrained("Jun421/MVP-3b") model = FiDT5.from_pretrained("Jun421/MVP-3b") - Notebooks
- Google Colab
- Kaggle
Add initial model card for MVP: Multi-view-guided Passage Reranking
#1
by nielsr HF Staff - opened
This PR adds an initial model card for the MVP model, based on the paper "Multi-view-guided Passage Reranking with Large Language Models".
It includes:
- A link to the paper: Multi-view-guided Passage Reranking with Large Language Models.
- The
license(Apache 2.0). - The
library_name(transformers), which enables the automated "how to use" widget on the Hub page due to the model's compatibility with the library. - The
pipeline_tag(text-ranking), making the model discoverable at https://huggingface.co/models?pipeline_tag=text-ranking. - The
base_model(google/t5-3b) for better context. - An overview of the model from the paper abstract and GitHub README.
- Setup and usage instructions directly from the GitHub repository.
- Links to available model checkpoints and datasets.
- A BibTeX citation.
Please review and merge this PR if everything looks good.