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
Quick Links
FiD model trained on NQ
-- 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].
-- Hyperparameters: 8 x 40GB A100 GPUs; batch size 8; AdamW; LR 3e-5; 50000 steps
References:
[1] Natural Questions: A Benchmark for Question Answering Research. TACL 2019.
[2] Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering. EACL 2021.
Model performance
We evaluate it on the NQ dataset, the EM score is 51.3 (0.1 lower than original performance reported in the paper).
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# Load model directly from transformers import AutoTokenizer, FiDT5 tokenizer = AutoTokenizer.from_pretrained("wyu1/FiD-NQ") model = FiDT5.from_pretrained("wyu1/FiD-NQ")