Instructions to use wyu1/FiD-NQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wyu1/FiD-NQ with Transformers:
# Load model directly from transformers import AutoTokenizer, FiDT5 tokenizer = AutoTokenizer.from_pretrained("wyu1/FiD-NQ") model = FiDT5.from_pretrained("wyu1/FiD-NQ") - Notebooks
- Google Colab
- Kaggle
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README.md
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# FiD model trained on NQ
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-- This is the model checkpoint of FiD, based on T5 large (with 770M parameters) and trained on the NQ dataset.
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-- Hyperparameters: 8 x 40GB A100 GPUs; batch size 8; AdamW; LR 3e-5; 50000 steps
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References:
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[1] Natural Questions: A Benchmark for Question Answering Research. TACL 2019.
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[2] Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering. EACL 2021.
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## Model performance
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# FiD model trained on NQ
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-- This is the model checkpoint of FiD [2], based on the T5 large (with 770M parameters) and trained on the natural question (NQ) dataset [1].
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-- Hyperparameters: 8 x 40GB A100 GPUs; batch size 8; AdamW; LR 3e-5; 50000 steps
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References:
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[1] Natural Questions: A Benchmark for Question Answering Research. TACL 2019.
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[2] Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering. EACL 2021.
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## Model performance
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