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scenario-NON-KD-PR-COPY-CDF-CL-D2_data-cl-cardiff_cl_only_alpha

Text Classification
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PyTorch
xlm-roberta
Generated from Trainer
Inference Endpoints
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scenario-NON-KD-PR-COPY-CDF-CL-D2_data-cl-cardiff_cl_only_alpha / README.md
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metadata
license: mit
base_model: xlm-roberta-base
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
model-index:
  - name: scenario-NON-KD-PR-COPY-CDF-CL-D2_data-cl-cardiff_cl_only_alpha
    results: []

scenario-NON-KD-PR-COPY-CDF-CL-D2_data-cl-cardiff_cl_only_alpha

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

  • Loss: 5.4350
  • Accuracy: 0.4537
  • F1: 0.4493

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: 5e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 1123
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 30

Training results

Training Loss Epoch Step Validation Loss Accuracy F1
No log 1.09 250 1.2630 0.4761 0.4753
0.8829 2.17 500 1.4325 0.4529 0.4468
0.8829 3.26 750 1.7675 0.4282 0.4077
0.538 4.35 1000 1.9149 0.4298 0.4134
0.538 5.43 1250 2.1555 0.4552 0.4471
0.2691 6.52 1500 2.7676 0.4637 0.4600
0.2691 7.61 1750 2.5963 0.4491 0.4431
0.1585 8.7 2000 3.0069 0.4437 0.4364
0.1585 9.78 2250 2.9894 0.4645 0.4621
0.1083 10.87 2500 3.6542 0.4537 0.4465
0.1083 11.96 2750 3.5648 0.4684 0.4695
0.0745 13.04 3000 3.7903 0.4761 0.4771
0.0745 14.13 3250 4.0663 0.4645 0.4623
0.0545 15.22 3500 4.1840 0.4830 0.4837
0.0545 16.3 3750 4.3967 0.4653 0.4640
0.04 17.39 4000 4.3967 0.4738 0.4714
0.04 18.48 4250 4.5309 0.4637 0.4600
0.0291 19.57 4500 4.5025 0.4715 0.4711
0.0291 20.65 4750 4.9500 0.4576 0.4554
0.0217 21.74 5000 5.0171 0.4498 0.4443
0.0217 22.83 5250 4.8470 0.4622 0.4618
0.018 23.91 5500 4.9003 0.4699 0.4698
0.018 25.0 5750 5.1935 0.4522 0.4491
0.0132 26.09 6000 5.1915 0.4630 0.4631
0.0132 27.17 6250 5.3879 0.4529 0.4478
0.0077 28.26 6500 5.2970 0.4637 0.4628
0.0077 29.35 6750 5.4350 0.4537 0.4493

Framework versions

  • Transformers 4.33.3
  • Pytorch 2.1.1+cu121
  • Datasets 2.14.5
  • Tokenizers 0.13.3