Instructions to use castorini/wiki-text-6-3-fid-large-tqa-reader with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use castorini/wiki-text-6-3-fid-large-tqa-reader with Transformers:
# Load model directly from transformers import AutoTokenizer, FiDT5 tokenizer = AutoTokenizer.from_pretrained("castorini/wiki-text-6-3-fid-large-tqa-reader") model = FiDT5.from_pretrained("castorini/wiki-text-6-3-fid-large-tqa-reader") - Notebooks
- Google Colab
- Kaggle
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2311b4e | 1 2 3 4 5 6 7 8 9 10 | Fusion-in-Decoder (FiD) is a model described in the following paper:
> Izacard, Gautier, and Édouard Grave. [Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering](https://aclanthology.org/2021.eacl-main.74/). _Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume_. 2021.
We have replicated FiD training with our Wikipedia corpus variants and incorporated the model into our [PyGaggle](https://github.com/castorini/pygaggle) neural text ranking library.
Our own efforts are described in the paper entitled:
> Pre-Processing Matters! Improved Wikipedia Corpora for Open-Domain Question Answering.
This is a FiD-large reader model for the wiki-text-6-3 corpus variant trained on the TriviaQA dataset. |