tes1-UASNLP2 / README.md
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metadata
license: apache-2.0
base_model: google-bert/bert-base-multilingual-uncased
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: tes1-UASNLP2
    results: []

tes1-UASNLP2

This model is a fine-tuned version of google-bert/bert-base-multilingual-uncased on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3413
  • Accuracy: 0.8705
  • Precision: 0.8858
  • Recall: 0.8803
  • F1: 0.8830

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: 64
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1
0.4596 1.2121 100 0.3631 0.8554 0.8767 0.8605 0.8685
0.254 2.4242 200 0.3413 0.8705 0.8858 0.8803 0.8830
0.1674 3.6364 300 0.3847 0.8793 0.8758 0.9118 0.8934
0.0968 4.8485 400 0.4927 0.8759 0.9145 0.8564 0.8845
0.0614 6.0606 500 0.5308 0.8721 0.8748 0.8981 0.8863
0.0418 7.2727 600 0.6098 0.8759 0.8988 0.8748 0.8867
0.0296 8.4848 700 0.6507 0.8751 0.8910 0.8830 0.8870
0.0183 9.6970 800 0.6822 0.8789 0.8944 0.8865 0.8904

Framework versions

  • Transformers 4.41.2
  • Pytorch 2.3.0+cu121
  • Tokenizers 0.19.1