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lilt-en-funsd

Token Classification
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PyTorch
lilt
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metadata
license: mit
base_model: SCUT-DLVCLab/lilt-roberta-en-base
tags:
  - generated_from_trainer
datasets:
  - funsd-layoutlmv3
model-index:
  - name: lilt-en-funsd
    results: []

lilt-en-funsd

This model is a fine-tuned version of SCUT-DLVCLab/lilt-roberta-en-base on the funsd-layoutlmv3 dataset. It achieves the following results on the evaluation set:

  • eval_loss: 1.9291
  • eval_ANSWER: {'precision': 0.0166358595194085, 'recall': 0.044063647490820076, 'f1': 0.024152968802415294, 'number': 817}
  • eval_HEADER: {'precision': 0.004098360655737705, 'recall': 0.008403361344537815, 'f1': 0.005509641873278237, 'number': 119}
  • eval_QUESTION: {'precision': 0.08307501549907005, 'recall': 0.2488393686165274, 'f1': 0.12456425749477108, 'number': 1077}
  • eval_overall_precision: 0.0541
  • eval_overall_recall: 0.1515
  • eval_overall_f1: 0.0798
  • eval_overall_accuracy: 0.1625
  • eval_runtime: 4.0045
  • eval_samples_per_second: 12.486
  • eval_steps_per_second: 1.748
  • step: 0

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: 5e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • training_steps: 2500

Framework versions

  • Transformers 4.34.0
  • Pytorch 2.1.0+cu118
  • Datasets 2.14.5
  • Tokenizers 0.14.1