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cs221-mDeBERTa-v3-base-mnli-xnli-finetuned-20-epochs

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metadata
library_name: transformers
license: mit
base_model: MoritzLaurer/mDeBERTa-v3-base-mnli-xnli
tags:
  - generated_from_trainer
metrics:
  - f1
  - accuracy
model-index:
  - name: cs221-mDeBERTa-v3-base-mnli-xnli-finetuned-20-epochs
    results: []

cs221-mDeBERTa-v3-base-mnli-xnli-finetuned-20-epochs

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

  • Loss: 0.5558
  • F1: 0.7088
  • Roc Auc: 0.7803
  • Accuracy: 0.3953

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: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 100
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss F1 Roc Auc Accuracy
0.6081 1.0 64 0.5854 0.4511 0.6250 0.1759
0.5318 2.0 128 0.5056 0.5413 0.6739 0.2905
0.4634 3.0 192 0.4846 0.6178 0.7158 0.3103
0.417 4.0 256 0.4696 0.6609 0.7452 0.3340
0.3762 5.0 320 0.4645 0.6707 0.7522 0.3577
0.3187 6.0 384 0.4622 0.6802 0.7591 0.3636
0.2801 7.0 448 0.4691 0.6953 0.7702 0.3557
0.2593 8.0 512 0.4847 0.6833 0.7614 0.3498
0.2278 9.0 576 0.4878 0.6941 0.7691 0.3775
0.1917 10.0 640 0.5014 0.7001 0.7735 0.3834
0.1761 11.0 704 0.5298 0.7020 0.7747 0.3893
0.1494 12.0 768 0.5417 0.7009 0.7741 0.3972
0.1422 13.0 832 0.5437 0.7074 0.7791 0.3953
0.1132 14.0 896 0.5602 0.6965 0.7709 0.3794
0.108 15.0 960 0.5558 0.7088 0.7803 0.3953
0.0945 16.0 1024 0.5700 0.7026 0.7753 0.3913
0.1004 17.0 1088 0.5748 0.7055 0.7776 0.3953
0.0857 18.0 1152 0.5733 0.7056 0.7776 0.3913
0.0893 19.0 1216 0.5731 0.7032 0.7759 0.3913
0.0881 20.0 1280 0.5735 0.7040 0.7765 0.3913

Framework versions

  • Transformers 4.47.1
  • Pytorch 2.5.1+cu121
  • Datasets 3.2.0
  • Tokenizers 0.21.0