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scenario-KD-PR-MSV-D2_data-cl-cardiff_cl_only_delta-jason

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PyTorch
xlm-roberta
Generated from Trainer
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scenario-KD-PR-MSV-D2_data-cl-cardiff_cl_only_delta-jason / README.md
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metadata
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
model-index:
  - name: scenario-KD-PR-MSV-D2_data-cl-cardiff_cl_only_delta-jason
    results: []

scenario-KD-PR-MSV-D2_data-cl-cardiff_cl_only_delta-jason

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

  • Loss: 17.1445
  • Accuracy: 0.4005
  • F1: 0.3981

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: 7777
  • 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 11.9607 0.3603 0.3165
13.9882 2.17 500 11.5568 0.375 0.3692
13.9882 3.26 750 11.3711 0.4090 0.4091
11.3923 4.35 1000 11.5830 0.3974 0.3972
11.3923 5.43 1250 11.6052 0.4074 0.4019
9.8435 6.52 1500 11.6147 0.4128 0.4100
9.8435 7.61 1750 12.7909 0.3966 0.3837
8.6495 8.7 2000 11.8204 0.4128 0.4046
8.6495 9.78 2250 12.4905 0.3889 0.3824
7.3906 10.87 2500 13.3672 0.4113 0.4097
7.3906 11.96 2750 15.0103 0.4020 0.3925
6.4797 13.04 3000 14.2666 0.3789 0.3791
6.4797 14.13 3250 14.9095 0.3904 0.3825
5.5862 15.22 3500 14.1357 0.4028 0.3995
5.5862 16.3 3750 15.0102 0.4005 0.3997
5.0029 17.39 4000 15.1376 0.4051 0.4035
5.0029 18.48 4250 15.2697 0.3958 0.3935
4.4091 19.57 4500 15.8735 0.3935 0.3905
4.4091 20.65 4750 15.5799 0.4028 0.4009
3.9734 21.74 5000 16.0068 0.4151 0.4137
3.9734 22.83 5250 15.9701 0.3796 0.3797
3.569 23.91 5500 16.0636 0.3850 0.3783
3.569 25.0 5750 16.2960 0.3904 0.3878
3.2517 26.09 6000 16.5689 0.3897 0.3850
3.2517 27.17 6250 17.1440 0.3858 0.3843
3.1047 28.26 6500 17.0026 0.4074 0.4028
3.1047 29.35 6750 17.1445 0.4005 0.3981

Framework versions

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