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scenario-TCR-XLMV_data-en-cardiff_eng_only_alpha2

Text Classification
Transformers
PyTorch
xlm-roberta
Generated from Trainer
Inference Endpoints
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scenario-TCR-XLMV_data-en-cardiff_eng_only_alpha2 / 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_alpha2
    results: []

scenario-TCR-XLMV_data-en-cardiff_eng_only_alpha2

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: 3.6119
  • Accuracy: 0.5472
  • F1: 0.5508

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: 24
  • 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 1.0411 0.4652 0.4291
No log 2.07 120 1.0685 0.5035 0.4475
No log 3.1 180 1.1001 0.5485 0.5474
No log 4.14 240 1.0647 0.5516 0.5549
No log 5.17 300 1.2458 0.5489 0.5528
No log 6.21 360 1.2913 0.5719 0.5717
No log 7.24 420 1.5986 0.5437 0.5459
No log 8.28 480 1.6908 0.5498 0.5510
0.641 9.31 540 1.7310 0.5582 0.5587
0.641 10.34 600 1.9959 0.5388 0.5394
0.641 11.38 660 2.2660 0.5357 0.5401
0.641 12.41 720 2.3724 0.5507 0.5543
0.641 13.45 780 2.5843 0.5450 0.5464
0.641 14.48 840 2.7003 0.5534 0.5556
0.641 15.52 900 2.7255 0.5459 0.5491
0.641 16.55 960 2.9127 0.5481 0.5504
0.1116 17.59 1020 2.9543 0.5432 0.5462
0.1116 18.62 1080 3.0564 0.5560 0.5591
0.1116 19.66 1140 3.1501 0.5494 0.5530
0.1116 20.69 1200 3.2882 0.5467 0.5507
0.1116 21.72 1260 3.3562 0.5459 0.5496
0.1116 22.76 1320 3.4030 0.5538 0.5573
0.1116 23.79 1380 3.4897 0.5489 0.5523
0.1116 24.83 1440 3.5540 0.5476 0.5508
0.0147 25.86 1500 3.5772 0.5498 0.5530
0.0147 26.9 1560 3.6123 0.5481 0.5515
0.0147 27.93 1620 3.5954 0.5494 0.5529
0.0147 28.97 1680 3.6081 0.5489 0.5524
0.0147 30.0 1740 3.6119 0.5472 0.5508

Framework versions

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