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scenario-TCR-XLMV_data-en-cardiff_eng_only_gamma2

Text Classification
Transformers
PyTorch
xlm-roberta
Generated from Trainer
Inference Endpoints
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scenario-TCR-XLMV_data-en-cardiff_eng_only_gamma2 / 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-en-cardiff_eng_only_gamma2
    results: []

scenario-TCR-XLMV_data-en-cardiff_eng_only_gamma2

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: 3.4094
  • Accuracy: 0.5516
  • F1: 0.5553

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: 77
  • 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 1.03 60 1.0398 0.4828 0.3902
No log 2.07 120 1.1798 0.4489 0.3679
No log 3.1 180 1.0463 0.4868 0.4351
No log 4.14 240 1.0244 0.5622 0.5553
No log 5.17 300 1.0819 0.5595 0.5478
No log 6.21 360 1.4170 0.5410 0.5407
No log 7.24 420 1.4249 0.5617 0.5653
No log 8.28 480 1.6285 0.5626 0.5627
0.6824 9.31 540 1.8719 0.5494 0.5516
0.6824 10.34 600 1.9037 0.5547 0.5574
0.6824 11.38 660 1.7645 0.5494 0.5516
0.6824 12.41 720 2.0301 0.5437 0.5459
0.6824 13.45 780 2.6619 0.5317 0.5330
0.6824 14.48 840 2.5606 0.5498 0.5520
0.6824 15.52 900 2.9065 0.5326 0.5347
0.6824 16.55 960 2.6860 0.5564 0.5597
0.132 17.59 1020 2.9277 0.5476 0.5495
0.132 18.62 1080 3.1905 0.5441 0.5472
0.132 19.66 1140 2.9974 0.5410 0.5446
0.132 20.69 1200 2.8902 0.5556 0.5575
0.132 21.72 1260 3.2156 0.5401 0.5432
0.132 22.76 1320 3.2772 0.5472 0.5501
0.132 23.79 1380 3.2211 0.5551 0.5569
0.132 24.83 1440 3.3844 0.5423 0.5450
0.0295 25.86 1500 3.3534 0.5494 0.5531
0.0295 26.9 1560 3.4030 0.5498 0.5534
0.0295 27.93 1620 3.4206 0.5511 0.5547
0.0295 28.97 1680 3.4273 0.5529 0.5565
0.0295 30.0 1740 3.4094 0.5516 0.5553

Framework versions

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