rob-base-superqa
This model is a fine-tuned version of roberta-base on the None dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 256
- total_eval_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
Framework versions
- Transformers 4.21.1
- Pytorch 1.11.0a0+gita4c10ee
- Datasets 2.4.0
- Tokenizers 0.12.1
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Datasets used to train nbroad/rob-base-superqa1
Evaluation results
- Exact Match on adversarial_qavalidation set self-reported43.867
- F1 on adversarial_qavalidation set self-reported55.135
- Exact Match on squad_v2validation set self-reported79.243
- F1 on squad_v2validation set self-reported82.336
- Exact Match on quorefvalidation set self-reported78.858
- F1 on quorefvalidation set self-reported82.826