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mDeBERTa-v3-base-xnli-multilingual-zeroshot-v2.0

Zero-Shot Classification
PyTorch
Chinese
deberta-v2
Generated from Trainer
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metadata
license: mit
base_model: MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: mDeBERTa-v3-base-xnli-multilingual-nli-2mil7
    results: []
datasets:
  - asadfgglie/nli-zh-tw-all
  - asadfgglie/BanBan_2024-10-17-facial_expressions-nli
language:
  - zh
pipeline_tag: zero-shot-classification

mDeBERTa-v3-base-xnli-multilingual-nli-2mil7

This model is a fine-tuned version of MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3496
  • F1 Macro: 0.8808
  • F1 Micro: 0.8813
  • Accuracy Balanced: 0.8806
  • Accuracy: 0.8813
  • Precision Macro: 0.8810
  • Recall Macro: 0.8806
  • Precision Micro: 0.8813
  • Recall Micro: 0.8813

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: 2e-05
  • train_batch_size: 16
  • eval_batch_size: 128
  • seed: 20241201
  • gradient_accumulation_steps: 2
  • 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
0.4669 0.17 200 0.4194 0.8011 0.8015 0.8068 0.8015 0.8029 0.8068 0.8015 0.8015
0.3921 0.34 400 0.4010 0.8139 0.8205 0.8095 0.8205 0.8283 0.8095 0.8205 0.8205
0.3468 0.51 600 0.3457 0.8459 0.8486 0.8445 0.8486 0.8478 0.8445 0.8486 0.8486
0.3299 0.68 800 0.3523 0.8595 0.8613 0.8598 0.8613 0.8593 0.8598 0.8613 0.8613
0.3192 0.85 1000 0.3372 0.8570 0.8592 0.8563 0.8592 0.8578 0.8563 0.8592 0.8592
0.3063 1.02 1200 0.3502 0.8594 0.8602 0.8627 0.8602 0.8585 0.8627 0.8602 0.8602
0.2481 1.19 1400 0.3579 0.8600 0.8624 0.8589 0.8624 0.8615 0.8589 0.8624 0.8624
0.2447 1.35 1600 0.3617 0.8636 0.8650 0.8649 0.8650 0.8628 0.8649 0.8650 0.8650
0.2496 1.52 1800 0.3494 0.8658 0.8677 0.8654 0.8677 0.8661 0.8654 0.8677 0.8677
0.2444 1.69 2000 0.3345 0.8644 0.8666 0.8635 0.8666 0.8656 0.8635 0.8666 0.8666
0.2217 1.86 2200 0.3452 0.8714 0.8724 0.8737 0.8724 0.8703 0.8737 0.8724 0.8724
0.2149 2.03 2400 0.3673 0.8727 0.8740 0.8737 0.8740 0.8719 0.8737 0.8740 0.8740
0.166 2.2 2600 0.3971 0.8731 0.8751 0.8723 0.8751 0.8741 0.8723 0.8751 0.8751
0.1685 2.37 2800 0.3884 0.8696 0.8714 0.8693 0.8714 0.8698 0.8693 0.8714 0.8714
0.1737 2.54 3000 0.3896 0.8674 0.8692 0.8672 0.8692 0.8676 0.8672 0.8692 0.8692
0.1667 2.71 3200 0.3950 0.8718 0.8735 0.8717 0.8735 0.8718 0.8717 0.8735 0.8735
0.1811 2.88 3400 0.3889 0.8707 0.8724 0.8708 0.8724 0.8707 0.8708 0.8724 0.8724

Eval result

Datasets asadfgglie/nli-zh-tw-all/test asadfgglie/BanBan_2024-10-17-facial_expressions-nli/test eval_dataset test_dataset
eval_loss 0.365 0.29 0.389 0.35
eval_f1_macro 0.875 0.911 0.87 0.881
eval_f1_micro 0.876 0.911 0.871 0.881
eval_accuracy_balanced 0.875 0.911 0.87 0.881
eval_accuracy 0.876 0.911 0.871 0.881
eval_precision_macro 0.875 0.912 0.87 0.881
eval_recall_macro 0.875 0.911 0.87 0.881
eval_precision_micro 0.876 0.911 0.871 0.881
eval_recall_micro 0.876 0.911 0.871 0.881
eval_runtime 232.017 4.063 51.192 204.15
eval_samples_per_second 36.635 232.844 36.9 37.017
eval_steps_per_second 0.289 1.969 0.293 0.294
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