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scenario-TCR-XLMV_data-cl-cardiff_cl_only_gamma

Text Classification
Transformers
PyTorch
xlm-roberta
Generated from Trainer
Inference Endpoints
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scenario-TCR-XLMV_data-cl-cardiff_cl_only_gamma / README.md
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metadata
license: mit
base_model: facebook/xlm-v-base
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
model-index:
  - name: scenario-TCR-XLMV_data-cl-cardiff_cl_only_gamma
    results: []

scenario-TCR-XLMV_data-cl-cardiff_cl_only_gamma

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

  • Loss: 1.0986
  • Accuracy: 0.3333
  • F1: 0.1667

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: 11423
  • 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 1.1044 0.3333 0.1667
1.0991 2.17 500 1.0996 0.3333 0.1667
1.0991 3.26 750 1.0992 0.3333 0.1667
1.1001 4.35 1000 1.0986 0.3333 0.1667
1.1001 5.43 1250 1.0998 0.3333 0.1667
1.0995 6.52 1500 1.0986 0.3333 0.1667
1.0995 7.61 1750 1.0987 0.3333 0.1667
1.0994 8.7 2000 1.0993 0.3333 0.1667
1.0994 9.78 2250 1.0996 0.3333 0.1667
1.0997 10.87 2500 1.0987 0.3333 0.1667
1.0997 11.96 2750 1.0989 0.3333 0.1667
1.0992 13.04 3000 1.0987 0.3333 0.1667
1.0992 14.13 3250 1.0987 0.3333 0.1667
1.0994 15.22 3500 1.0988 0.3333 0.1667
1.0994 16.3 3750 1.0987 0.3333 0.1667
1.0991 17.39 4000 1.0988 0.3333 0.1667
1.0991 18.48 4250 1.0986 0.3333 0.1667
1.0992 19.57 4500 1.0986 0.3333 0.1667
1.0992 20.65 4750 1.0986 0.3333 0.1667
1.099 21.74 5000 1.0987 0.3333 0.1667
1.099 22.83 5250 1.0986 0.3333 0.1667
1.0991 23.91 5500 1.0986 0.3333 0.1667
1.0991 25.0 5750 1.0986 0.3333 0.1667
1.0988 26.09 6000 1.0987 0.3333 0.1667
1.0988 27.17 6250 1.0986 0.3333 0.1667
1.0991 28.26 6500 1.0986 0.3333 0.1667
1.0991 29.35 6750 1.0986 0.3333 0.1667

Framework versions

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