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xlm-roberta-large-xnli-anli-v2.0

PyTorch
xlm-roberta
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metadata
license: mit
base_model: vicgalle/xlm-roberta-large-xnli-anli
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: xlm-roberta-large-xnli-anli
    results: []

xlm-roberta-large-xnli-anli

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

  • Loss: 0.3689
  • F1 Macro: 0.8721
  • F1 Micro: 0.8729
  • Accuracy Balanced: 0.8725
  • Accuracy: 0.8729
  • Precision Macro: 0.8718
  • Recall Macro: 0.8725
  • Precision Micro: 0.8729
  • Recall Micro: 0.8729

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: 9e-06
  • train_batch_size: 8
  • eval_batch_size: 64
  • seed: 40
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.06
  • num_epochs: 3

Training results

Training Loss Epoch Step Validation Loss F1 Macro F1 Micro Accuracy Balanced Accuracy Precision Macro Recall Macro Precision Micro Recall Micro
0.4942 0.17 200 0.4413 0.8081 0.8089 0.8093 0.8089 0.8076 0.8093 0.8089 0.8089
0.4114 0.34 400 0.3991 0.8227 0.8232 0.8250 0.8232 0.8226 0.8250 0.8232 0.8232
0.3467 0.51 600 0.3584 0.8388 0.8391 0.8421 0.8391 0.8396 0.8421 0.8391 0.8391
0.3402 0.68 800 0.3620 0.8534 0.8544 0.8536 0.8544 0.8532 0.8536 0.8544 0.8544
0.3304 0.85 1000 0.3385 0.8566 0.8576 0.8567 0.8576 0.8565 0.8567 0.8576 0.8576
0.3234 1.02 1200 0.3456 0.8637 0.8650 0.8631 0.8650 0.8645 0.8631 0.8650 0.8650
0.2702 1.19 1400 0.3201 0.8606 0.8613 0.8616 0.8613 0.8600 0.8616 0.8613 0.8613
0.2581 1.36 1600 0.3233 0.8619 0.8624 0.8639 0.8624 0.8615 0.8639 0.8624 0.8624
0.2414 1.52 1800 0.3451 0.8674 0.8687 0.8664 0.8687 0.8687 0.8664 0.8687 0.8687
0.2687 1.69 2000 0.3415 0.8577 0.8608 0.8544 0.8608 0.8677 0.8544 0.8608 0.8608
0.2518 1.86 2200 0.3378 0.8684 0.8692 0.8688 0.8692 0.8681 0.8688 0.8692 0.8692
0.2182 2.03 2400 0.3581 0.8698 0.8708 0.8697 0.8708 0.8700 0.8697 0.8708 0.8708
0.1919 2.2 2600 0.3671 0.8677 0.8687 0.8676 0.8687 0.8678 0.8676 0.8687 0.8687
0.1771 2.37 2800 0.3790 0.8709 0.8719 0.8707 0.8719 0.8710 0.8707 0.8719 0.8719
0.1793 2.54 3000 0.3856 0.8687 0.8692 0.8701 0.8692 0.8680 0.8701 0.8692 0.8692
0.1909 2.71 3200 0.3777 0.8686 0.8698 0.8682 0.8698 0.8691 0.8682 0.8698 0.8698
0.2021 2.88 3400 0.3685 0.8701 0.8708 0.8710 0.8708 0.8696 0.8710 0.8708 0.8708

eval result

Datasets asadfgglie/nli-zh-tw-all/test asadfgglie/BanBan_2024-10-17-facial_expressions-nli/test eval_dataset test_dataset
eval_loss 0.355 0.246 0.369 0.337
eval_f1_macro 0.872 0.932 0.872 0.88
eval_f1_micro 0.873 0.932 0.873 0.881
eval_accuracy_balanced 0.872 0.932 0.873 0.88
eval_accuracy 0.873 0.932 0.873 0.881
eval_precision_macro 0.873 0.932 0.872 0.881
eval_recall_macro 0.872 0.932 0.873 0.88
eval_precision_micro 0.873 0.932 0.873 0.881
eval_recall_micro 0.873 0.932 0.873 0.881
eval_runtime 50.724 0.611 11.126 44.342
eval_samples_per_second 167.574 1547.575 169.783 170.424
eval_steps_per_second 2.622 24.539 2.696 2.684
Size of dataset 8500 946 1889 7557

Framework versions

  • Transformers 4.33.3
  • Pytorch 2.5.1+cu121
  • Datasets 2.14.7
  • Tokenizers 0.13.3