LiLT-RE-SIN / README.md
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LiLT-RE-SIN
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metadata
license: mit
base_model: nielsr/lilt-xlm-roberta-base
tags:
  - generated_from_trainer
datasets:
  - xfun
metrics:
  - precision
  - recall
  - f1
model-index:
  - name: checkpoints
    results: []

checkpoints

This model is a fine-tuned version of nielsr/lilt-xlm-roberta-base on the xfun dataset. It achieves the following results on the evaluation set:

  • Precision: 0.2809
  • Recall: 0.5051
  • F1: 0.3610
  • Loss: 1.6168

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: 1e-05
  • train_batch_size: 8
  • eval_batch_size: 2
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • training_steps: 8000

Training results

Training Loss Epoch Step F1 Validation Loss Precision Recall
0.1546 41.67 500 0 0.2482 0 0
0.1674 83.33 1000 0 0.2477 0 0
0.1368 125.0 1500 0.1502 0.2256 0.1975 0.1212
0.0727 166.67 2000 0.2732 0.3218 0.2091 0.3939
0.0718 208.33 2500 0.3385 0.3518 0.2579 0.4924
0.0612 250.0 3000 0.3371 0.5235 0.2555 0.4949
0.0504 291.67 3500 0.3353 0.5280 0.2536 0.4949
0.0418 333.33 4000 0.3476 0.6919 0.2657 0.5025
0.0308 375.0 4500 0.3490 0.7819 0.2613 0.5253
0.039 416.67 5000 0.3463 1.0291 0.2627 0.5076
0.0301 458.33 5500 0.3443 1.1661 0.2626 0.5
0.0245 500.0 6000 0.3414 1.2341 0.2642 0.4823
0.0347 541.67 6500 0.3389 1.4114 0.2605 0.4848
0.0327 583.33 7000 0.3422 1.4326 0.2683 0.4722
0.0117 625.0 7500 0.3670 1.6092 0.2899 0.5
0.0255 666.67 8000 0.3607 1.6141 0.2805 0.5051

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.1.0+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.1