Revision_Meta_XLM

This model is a fine-tuned version of FacebookAI/xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4710
  • Accuracy: 0.9220
  • F1: 0.7251

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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • num_epochs: 15

Training results

Training Loss Epoch Step Validation Loss Accuracy F1
No log 0.5102 150 0.3977 0.8526 0.4695
0.5230 1.0204 300 0.2417 0.9188 0.7149
0.5230 1.5306 450 0.2309 0.9220 0.7111
0.2723 2.0408 600 0.2386 0.9180 0.7130
0.2723 2.5510 750 0.2537 0.9259 0.7229
0.2107 3.0612 900 0.2225 0.9354 0.7547
0.2107 3.5714 1050 0.2462 0.9330 0.7376
0.1682 4.0816 1200 0.3432 0.9117 0.7101
0.1682 4.5918 1350 0.2572 0.9267 0.7195
0.1499 5.1020 1500 0.3172 0.9243 0.7344
0.1499 5.6122 1650 0.3080 0.9212 0.7206
0.1219 6.1224 1800 0.3767 0.9133 0.6921
0.1219 6.6327 1950 0.4346 0.9039 0.7024
0.1055 7.1429 2100 0.3059 0.9338 0.7309
0.1055 7.6531 2250 0.3716 0.9236 0.7237
0.0786 8.1633 2400 0.4106 0.9188 0.7174
0.0786 8.6735 2550 0.4007 0.9228 0.7250
0.0673 9.1837 2700 0.4195 0.9236 0.7309
0.0673 9.6939 2850 0.4486 0.9149 0.7102
0.0589 10.2041 3000 0.5117 0.9078 0.6939
0.0589 10.7143 3150 0.4755 0.9149 0.7154
0.0494 11.2245 3300 0.4287 0.9204 0.7198
0.0494 11.7347 3450 0.3905 0.9314 0.7322
0.0426 12.2449 3600 0.4332 0.9243 0.7333
0.0426 12.7551 3750 0.4228 0.9307 0.7385
0.0339 13.2653 3900 0.4589 0.9275 0.7380
0.0339 13.7755 4050 0.5148 0.9141 0.7128
0.0272 14.2857 4200 0.4624 0.9236 0.7273
0.0272 14.7959 4350 0.4710 0.9220 0.7251

Framework versions

  • Transformers 5.3.0
  • Pytorch 2.9.0+cu126
  • Datasets 4.0.0
  • Tokenizers 0.22.2
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