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xlm-v-base-finetuned-xglue-xnli

Text Classification
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xlm-roberta
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xnli
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xlm-v-base-finetuned-xglue-xnli / README.md
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metadata
license: mit
langs:
  - multilingual
tags:
  - generated_from_trainer
  - xnli
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) fine-tuned on XNLI

This model is a fine-tuned version of XLM-V (base) on the XNLI (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