rob-base-gc1
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: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: IPU
- gradient_accumulation_steps: 64
- total_train_batch_size: 256
- total_eval_batch_size: 20
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2.0
- training precision: Mixed Precision
Training results
Framework versions
- Transformers 4.20.0
- Pytorch 1.10.0+cpu
- Datasets 2.4.0
- Tokenizers 0.12.1
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Datasets used to train nbroad/rob-base-gc1
Evaluation results
- Exact Match on adversarial_qavalidation set self-reported42.900
- F1 on adversarial_qavalidation set self-reported53.895
- Exact Match on squad_v2validation set self-reported79.538
- F1 on squad_v2validation set self-reported82.722
- Exact Match on quorefvalidation set self-reported78.403
- F1 on quorefvalidation set self-reported82.141