Hugging Face's logo

mrm8488
/
xlm-v-base-finetuned-xglue-xnli

Text Classification
Transformers
PyTorch
TensorBoard
xlm-roberta
Generated from Trainer
xnli
Eval Results
Inference Endpoints
Model card Files Metrics Community
1
xlm-v-base-finetuned-xglue-xnli / README.md
mrm8488's picture
mrm8488
End of training
d264e50
preview code
|
raw history blame
3.33 kB
metadata
license: mit
tags:
  - generated_from_trainer
datasets:
  - xglue
metrics:
  - accuracy
model-index:
  - name: xlm-v-base-finetuned-xglue-xnli
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: xglue
          type: xglue
          config: xnli
          split: >-
            validation.en+validation.ar+validation.bg+validation.de+validation.el+validation.es+validation.fr+validation.hi+validation.ru+validation.sw+validation.th+validation.tr+validation.ur+validation.vi+validation.zh
          args: xnli
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7402677376171352

xlm-v-base-finetuned-xglue-xnli

This model is a fine-tuned version of stefan-it/xlm-v-base on the xglue dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6511
  • Accuracy: 0.7403

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 2
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.0994 0.08 1000 1.0966 0.3697
1.0221 0.16 2000 1.0765 0.4560
0.8437 0.24 3000 0.8472 0.6179
0.6997 0.33 4000 0.7650 0.6804
0.6304 0.41 5000 0.7227 0.7007
0.5972 0.49 6000 0.7430 0.6977
0.5886 0.57 7000 0.7365 0.7066
0.5585 0.65 8000 0.6819 0.7223
0.5464 0.73 9000 0.7222 0.7046
0.5289 0.81 10000 0.7290 0.7054
0.5298 0.9 11000 0.6824 0.7221
0.5241 0.98 12000 0.6650 0.7268
0.4806 1.06 13000 0.6861 0.7308
0.4715 1.14 14000 0.6619 0.7304
0.4645 1.22 15000 0.6656 0.7284
0.4443 1.3 16000 0.7026 0.7270
0.4582 1.39 17000 0.7055 0.7225
0.4456 1.47 18000 0.6592 0.7361
0.44 1.55 19000 0.6816 0.7329
0.4419 1.63 20000 0.6772 0.7357
0.4403 1.71 21000 0.6745 0.7319
0.4348 1.79 22000 0.6678 0.7338
0.4355 1.87 23000 0.6614 0.7365
0.4295 1.96 24000 0.6511 0.7403

Framework versions

  • Transformers 4.26.0
  • Pytorch 1.13.1+cu116
  • Datasets 2.9.0
  • Tokenizers 0.13.2