xlm-roberta-large-xnli-v4.0

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

  • Loss: 0.4963
  • F1 Macro: 0.8192
  • F1 Micro: 0.8204
  • Accuracy Balanced: 0.8190
  • Accuracy: 0.8204
  • Precision Macro: 0.8193
  • Recall Macro: 0.8190
  • Precision Micro: 0.8204
  • Recall Micro: 0.8204

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
0.3593 1.69 200 0.4297 0.8211 0.8218 0.8224 0.8218 0.8206 0.8224 0.8218 0.8218

eval result

Datasets asadfgglie/nli-zh-tw-all/test asadfgglie/BanBan_2024-10-17-facial_expressions-nli/test eval_dataset test_dataset
eval_loss 0.494 0.773 0.483 0.496
eval_f1_macro 0.821 0.627 0.825 0.819
eval_f1_micro 0.822 0.644 0.826 0.82
eval_accuracy_balanced 0.821 0.638 0.826 0.819
eval_accuracy 0.822 0.644 0.826 0.82
eval_precision_macro 0.821 0.663 0.825 0.819
eval_recall_macro 0.821 0.638 0.826 0.819
eval_precision_micro 0.822 0.644 0.826 0.82
eval_recall_micro 0.822 0.644 0.826 0.82
eval_runtime 50.82 0.635 10.346 39.781
eval_samples_per_second 167.257 1490.523 164.308 170.938
eval_steps_per_second 2.617 23.634 2.61 2.69
Size of dataset 8500 946 1700 6800

Framework versions

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