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dadcd61
metadata
license: mit
tags:
  - generated_from_trainer
datasets:
  - commonsense_qa
metrics:
  - accuracy
model_index:
  - name: roberta-large-finetuned-csqa
    results:
      - dataset:
          name: commonsense_qa
          type: commonsense_qa
          args: default
        metric:
          name: Accuracy
          type: accuracy
          value: 0.7330057621002197

roberta-large-finetuned-csqa

This model is a fine-tuned version of roberta-large on the commonsense_qa dataset. It achieves the following results on the evaluation set:

  • Loss: 0.9146
  • Accuracy: 0.7330

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: 1e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 5
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.3903 1.0 609 0.8845 0.6642
0.8939 2.0 1218 0.7054 0.7281
0.6163 3.0 1827 0.7452 0.7314
0.4245 4.0 2436 0.8369 0.7355
0.3258 5.0 3045 0.9146 0.7330

Framework versions

  • Transformers 4.9.0
  • Pytorch 1.9.0
  • Datasets 1.10.2
  • Tokenizers 0.10.3