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implicit-motives-power-roberta-large

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roberta
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metadata
language:
  - en
license: mit
base_model: roberta-large
tags:
  - generated_from_trainer
datasets:
  - schone-power
model-index:
  - name: final
    results: []

final

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

  • Loss: 0.1556
  • Roc Auc: 0.9742

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: 128
  • eval_batch_size: 256
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 256
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 5.0

Training results

Training Loss Epoch Step Validation Loss Roc Auc
0.2924 0.9985 332 0.2605 0.9248
0.2553 2.0 665 0.2234 0.9468
0.2317 2.9985 997 0.1899 0.9620
0.2063 4.0 1330 0.1645 0.9715
0.1897 4.9925 1660 0.1556 0.9742

Framework versions

  • Transformers 4.44.0.dev0
  • Pytorch 2.0.1+cu118
  • Datasets 2.20.0
  • Tokenizers 0.19.1