This checkpoint is obtained after training FlaxBigBirdForQuestionAnswering (with extra pooler head) on natural_questions dataset on TPU v3-8. This dataset takes around ~100 GB on disk. But thanks to Cloud TPUs and Jax, each epoch took just 4.5 hours. Script for training can be found here: https://github.com/vasudevgupta7/bigbird
Use this model just like any other model from 🤗Transformers
from transformers import FlaxBigBirdForQuestionAnswering, BigBirdTokenizerFast
model_id = "vasudevgupta/flax-bigbird-natural-questions"
model = FlaxBigBirdForQuestionAnswering.from_pretrained(model_id)
tokenizer = BigBirdTokenizerFast.from_pretrained(model_id)
In case you are interested in predicting category (null, long, short, yes, no) as well, use FlaxBigBirdForNaturalQuestions (instead of FlaxBigBirdForQuestionAnswering) from my training script.
| Exact Match | 55.12 |
|---|
Evaluation script: https://colab.research.google.com/github/vasudevgupta7/bigbird/blob/main/notebooks/evaluate-flax-natural-questions.ipynb
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