xlm-roberta-large-finetuned-ner-geocorpus

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

  • Loss: 0.1447
  • Precision: 0.8446
  • Recall: 0.8970
  • F1: 0.8700
  • Accuracy: 0.9783

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: 2
  • eval_batch_size: 2
  • seed: 42
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 16
  • optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 0.9991 275 0.1397 0.7230 0.7245 0.7237 0.9581
0.2086 1.9982 550 0.1079 0.7256 0.8507 0.7832 0.9655
0.2086 2.9973 825 0.0866 0.8121 0.8496 0.8304 0.9733
0.0636 4.0 1101 0.0830 0.8030 0.9001 0.8488 0.9759
0.0636 4.9991 1376 0.1024 0.8433 0.8770 0.8598 0.9756
0.032 5.9982 1651 0.1036 0.84 0.8833 0.8611 0.9784
0.032 6.9973 1926 0.1132 0.8278 0.8948 0.8600 0.9752
0.0165 8.0 2202 0.1211 0.8473 0.8927 0.8694 0.9782
0.0165 8.9991 2477 0.1406 0.8402 0.8959 0.8672 0.9775
0.0089 9.9909 2750 0.1447 0.8446 0.8970 0.8700 0.9783

Framework versions

  • Transformers 4.46.2
  • Pytorch 2.5.1+cu121
  • Datasets 3.1.0
  • Tokenizers 0.20.3
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