metadata

license: mit
base_model: SCUT-DLVCLab/lilt-roberta-en-base
tags:
  - generated_from_trainer
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 an unknown dataset. It achieves the following results on the evaluation set:

eval_loss: 1.6706
eval_ANSWER: {'precision': 0.875, 'recall': 0.9082007343941249, 'f1': 0.8912912912912913, 'number': 817}
eval_HEADER: {'precision': 0.6666666666666666, 'recall': 0.5714285714285714, 'f1': 0.6153846153846153, 'number': 119}
eval_QUESTION: {'precision': 0.8880931065353626, 'recall': 0.9210770659238626, 'f1': 0.9042844120328168, 'number': 1077}
eval_overall_precision: 0.8718
eval_overall_recall: 0.8952
eval_overall_f1: 0.8833
eval_overall_accuracy: 0.8026
eval_runtime: 50.8863
eval_samples_per_second: 0.983
eval_steps_per_second: 0.138
epoch: 90.8421
step: 1726

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
mixed_precision_training: Native AMP

Framework versions

Transformers 4.42.4
Pytorch 2.3.1+cpu
Datasets 2.20.0
Tokenizers 0.19.1