Instructions to use wyu1/GenRead-3B-TQA-MergeDPR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wyu1/GenRead-3B-TQA-MergeDPR with Transformers:
# Load model directly from transformers import AutoTokenizer, FiDT5 tokenizer = AutoTokenizer.from_pretrained("wyu1/GenRead-3B-TQA-MergeDPR") model = FiDT5.from_pretrained("wyu1/GenRead-3B-TQA-MergeDPR") - Notebooks
- Google Colab
- Kaggle
GenRead (MergeDPR): FiD model trained on TQA
-- This is the model checkpoint of GenRead [2], based on the T5-3B and trained on the TriviaQA [1].
-- Hyperparameters: 8 x 80GB A100 GPUs; batch size 16; AdamW; LR 5e-5; best dev at 9000 steps
References:
[1] TriviaQA: A Large Scale Dataset for Reading Comprehension and Question Answering. ACL 2017
[2] Generate rather than Retrieve: Large Language Models are Strong Context Generators. arXiv 2022
Model performance
We evaluate it on the TriviaQA dataset, the EM score is 74.41.
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license: cc-by-4.0
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