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metadata
library_name: transformers
license: mit
base_model: roberta-large
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: wikidyk-scope-clf-deberta-v3-large-semantic_3_clusters
    results: []

wikidyk-scope-clf-deberta-v3-large-semantic_3_clusters

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

  • Loss: 0.5560
  • Accuracy: 0.9132
  • F1: 0.7401
  • Precision: 0.8592
  • Recall: 0.6501

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
  • distributed_type: multi-GPU
  • num_devices: 4
  • total_train_batch_size: 128
  • total_eval_batch_size: 128
  • 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: 10.0

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Precision Recall
0.0494 1.0 902 0.3398 0.8673 0.5190 0.8346 0.3766
0.0428 2.0 1804 0.3267 0.8902 0.6328 0.8696 0.4973
0.0292 3.0 2706 0.3243 0.9071 0.7333 0.8077 0.6714
0.0208 4.0 3608 0.4366 0.8926 0.6603 0.8284 0.5488
0.0096 5.0 4510 0.4438 0.8943 0.6829 0.7948 0.5986
0.0182 6.0 5412 0.5225 0.8950 0.6594 0.86 0.5346
0.0062 7.0 6314 0.4717 0.9078 0.7117 0.8776 0.5986
0.0021 8.0 7216 0.5226 0.9095 0.7254 0.8571 0.6288
0.0026 9.0 8118 0.5323 0.9173 0.7528 0.8715 0.6625
0.0008 10.0 9020 0.5560 0.9132 0.7401 0.8592 0.6501

Framework versions

  • Transformers 4.51.3
  • Pytorch 2.6.0+cu124
  • Datasets 3.5.1
  • Tokenizers 0.21.1