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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
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Dataset used to train mrm8488/xlm-v-base-finetuned-xglue-xnli

Evaluation results