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verdict-classifier-en

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roberta
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metadata
license: mit
tags:
  - generated_from_trainer
model-index:
  - name: verdict-classifier-en
    results: []

verdict-classifier-en

This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1520
  • F1 Macro: 0.9013
  • F1 Misinformation: 0.9841
  • F1 Factual: 0.9697
  • F1 Other: 0.75
  • Prec Macro: 0.8643
  • Prec Misinformation: 0.9954
  • Prec Factual: 0.9412
  • Prec Other: 0.6562

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: 2e-05
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 550
  • num_epochs: 1000

Training results

Training Loss Epoch Step Validation Loss F1 Macro F1 Misinformation F1 Factual F1 Other Prec Macro Prec Misinformation Prec Factual Prec Other
1.072 0.73 50 1.0233 0.3136 0.9408 0.0 0.0 0.2961 0.8882 0.0 0.0
1.0077 1.47 100 0.8870 0.3136 0.9408 0.0 0.0 0.2961 0.8882 0.0 0.0
0.9439 2.2 150 0.6889 0.3136 0.9408 0.0 0.0 0.2961 0.8882 0.0 0.0
0.8743 2.93 200 0.3857 0.3129 0.9386 0.0 0.0 0.2959 0.8878 0.0 0.0
0.7564 3.67 250 0.2474 0.4630 0.9716 0.0 0.4176 0.4225 0.9839 0.0 0.2836
0.5366 4.41 300 0.1819 0.8054 0.9713 0.8772 0.5676 0.8043 0.9930 1.0 0.42
0.4043 5.15 350 0.1344 0.8425 0.9738 0.9538 0.6 0.8093 0.9884 0.9394 0.5
0.3792 5.87 400 0.1259 0.8645 0.9761 0.9841 0.6333 0.8388 0.9885 1.0 0.5278
0.2756 6.61 450 0.1344 0.8576 0.9774 0.9538 0.6415 0.8366 0.9841 0.9394 0.5862
0.2589 7.35 500 0.1188 0.8738 0.9783 0.9412 0.7018 0.8293 0.9931 0.8889 0.6061
0.2175 8.09 550 0.1436 0.8573 0.9798 0.9538 0.6383 0.8571 0.9798 0.9394 0.6522
0.1888 8.81 600 0.1566 0.8613 0.9761 0.9412 0.6667 0.8185 0.9907 0.8889 0.5758
0.15 9.55 650 0.1549 0.8542 0.9773 0.9538 0.6316 0.8245 0.9885 0.9394 0.5455
0.1464 10.29 700 0.1608 0.8633 0.9773 0.9697 0.6429 0.8307 0.9885 0.9412 0.5625
0.0954 11.03 750 0.1520 0.9013 0.9841 0.9697 0.75 0.8643 0.9954 0.9412 0.6562
0.1074 11.76 800 0.1655 0.8810 0.9819 0.9552 0.7059 0.8565 0.9886 0.9143 0.6667
0.1078 12.49 850 0.1937 0.8989 0.9829 0.9552 0.7586 0.8530 0.9977 0.9143 0.6471
0.098 13.23 900 0.2098 0.8767 0.9794 0.9412 0.7097 0.8226 1.0 0.8889 0.5789
0.0931 13.96 950 0.1591 0.8755 0.9819 0.9538 0.6909 0.8477 0.9908 0.9394 0.6129
0.0701 14.7 1000 0.2121 0.8926 0.9805 0.9552 0.7419 0.8398 1.0 0.9143 0.6053
0.0692 15.44 1050 0.2118 0.8989 0.9829 0.9552 0.7586 0.8530 0.9977 0.9143 0.6471
0.0848 16.17 1100 0.2094 0.8913 0.9818 0.9552 0.7368 0.8487 0.9954 0.9143 0.6364
0.0471 16.9 1150 0.2197 0.8919 0.9818 0.9697 0.7241 0.8514 0.9954 0.9412 0.6176
0.0399 17.64 1200 0.1997 0.9019 0.9852 0.9538 0.7667 0.8594 1.0 0.9394 0.6389
0.0307 18.38 1250 0.2873 0.8830 0.9795 0.9697 0.7000 0.8400 0.9954 0.9412 0.5833

Framework versions

  • Transformers 4.11.3
  • Pytorch 1.9.0+cu102
  • Datasets 1.9.0
  • Tokenizers 0.10.2