transformers pytorch tensorboard roberta question-answering generated_from_trainer license:apache-2.0 endpoints_compatible region:us
---
license: apache-2.0
tags:
- question-answering
- generated_from_trainer
model-index:
- name: roberta-base-squad2-nq-bioasq
  results: []
---

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# roberta-base-squad2-nq-bioasq


## Model description

This model is a fine-tuned version of [nlpconnect/roberta-base-squad2-nq](https://huggingface.co/nlpconnect/roberta-base-squad2-nq) on the BioASQ 10b dataset.

## Intended uses & limitations

Cross-domain question answering!

## Training and evaluation data

Training: BioASQ 10B with SQUAD sampled evenly to match the same samples as BioASQ 10B
Eval: BioASQ 9B Eval with SQUAD Eval sampled evenly to match the same samples as BioASQ 9B Eval

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

Went from untrained exact match: 60.9% (f1 71.8%) to exact match: 95.2% (96.6% f1) on BioASQ 9B held out training set.
Scores on SQUAD+BioASQ remained stable at exact match: 72.5% (f1 81.4%) to 88.5% (f1 93.3%).


### Framework versions

- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
