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metadata
license: mit
tags:
  - generated_from_trainer
datasets:
  - banking77
metrics:
  - accuracy
model-index:
  - name: xlm-roberta-base-banking77-classification
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: banking77
          type: banking77
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9321428571428572

xlm-roberta-base-banking77-classification

This model is a fine-tuned version of xlm-roberta-base on the banking77 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3034
  • Accuracy: 0.9321
  • F1 Score: 0.9321

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: 64
  • eval_batch_size: 64
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Score
3.8002 1.0 157 2.7771 0.5159 0.4483
2.4006 2.0 314 1.6937 0.7140 0.6720
1.4633 3.0 471 1.0385 0.8308 0.8153
0.9234 4.0 628 0.7008 0.8789 0.8761
0.6163 5.0 785 0.5029 0.9068 0.9063
0.4282 6.0 942 0.4084 0.9123 0.9125
0.3203 7.0 1099 0.3515 0.9253 0.9253
0.245 8.0 1256 0.3295 0.9227 0.9225
0.1863 9.0 1413 0.3092 0.9269 0.9269
0.1518 10.0 1570 0.2901 0.9338 0.9338
0.1179 11.0 1727 0.2938 0.9318 0.9319
0.0969 12.0 1884 0.2906 0.9328 0.9328
0.0805 13.0 2041 0.2963 0.9295 0.9295
0.063 14.0 2198 0.2998 0.9289 0.9288
0.0554 15.0 2355 0.2933 0.9351 0.9349
0.046 16.0 2512 0.2960 0.9328 0.9326
0.04 17.0 2669 0.3032 0.9318 0.9318
0.035 18.0 2826 0.3061 0.9312 0.9312
0.0317 19.0 2983 0.3030 0.9331 0.9330
0.0315 20.0 3140 0.3034 0.9321 0.9321

Framework versions

  • Transformers 4.21.1
  • Pytorch 1.12.1+cu113
  • Datasets 2.4.0
  • Tokenizers 0.12.1