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---
license: mit
base_model: SCUT-DLVCLab/lilt-roberta-en-base
tags:
- generated_from_trainer
model-index:
- name: lilt-en-funsd
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# lilt-en-funsd
This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7177
- Answer: {'precision': 0.8729411764705882, 'recall': 0.9082007343941249, 'f1': 0.8902219556088783, 'number': 817}
- Header: {'precision': 0.6421052631578947, 'recall': 0.5126050420168067, 'f1': 0.5700934579439252, 'number': 119}
- Question: {'precision': 0.906021897810219, 'recall': 0.9220055710306406, 'f1': 0.9139438564196963, 'number': 1077}
- Overall Precision: 0.8800
- Overall Recall: 0.8922
- Overall F1: 0.8860
- Overall Accuracy: 0.8064
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:--------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.4044 | 10.5263 | 200 | 1.1266 | {'precision': 0.8246606334841629, 'recall': 0.8922888616891065, 'f1': 0.8571428571428571, 'number': 817} | {'precision': 0.3495575221238938, 'recall': 0.6638655462184874, 'f1': 0.4579710144927537, 'number': 119} | {'precision': 0.8747609942638623, 'recall': 0.8495821727019499, 'f1': 0.8619877531794631, 'number': 1077} | 0.7992 | 0.8559 | 0.8266 | 0.7671 |
| 0.0518 | 21.0526 | 400 | 1.2142 | {'precision': 0.8138006571741512, 'recall': 0.9094247246022031, 'f1': 0.8589595375722543, 'number': 817} | {'precision': 0.49612403100775193, 'recall': 0.5378151260504201, 'f1': 0.5161290322580645, 'number': 119} | {'precision': 0.8705234159779615, 'recall': 0.8802228412256268, 'f1': 0.8753462603878117, 'number': 1077} | 0.8236 | 0.8718 | 0.8470 | 0.8011 |
| 0.0137 | 31.5789 | 600 | 1.5789 | {'precision': 0.8478513356562137, 'recall': 0.8935128518971848, 'f1': 0.8700834326579261, 'number': 817} | {'precision': 0.5588235294117647, 'recall': 0.4789915966386555, 'f1': 0.5158371040723981, 'number': 119} | {'precision': 0.8839528558476881, 'recall': 0.9052924791086351, 'f1': 0.8944954128440367, 'number': 1077} | 0.8529 | 0.8753 | 0.8639 | 0.7932 |
| 0.008 | 42.1053 | 800 | 1.5466 | {'precision': 0.8540478905359179, 'recall': 0.9167686658506732, 'f1': 0.8842975206611571, 'number': 817} | {'precision': 0.528169014084507, 'recall': 0.6302521008403361, 'f1': 0.5747126436781609, 'number': 119} | {'precision': 0.899074074074074, 'recall': 0.9015784586815228, 'f1': 0.9003245248029671, 'number': 1077} | 0.8552 | 0.8917 | 0.8731 | 0.7876 |
| 0.0047 | 52.6316 | 1000 | 1.5218 | {'precision': 0.8712029161603888, 'recall': 0.8776009791921665, 'f1': 0.874390243902439, 'number': 817} | {'precision': 0.5882352941176471, 'recall': 0.5042016806722689, 'f1': 0.5429864253393665, 'number': 119} | {'precision': 0.9080459770114943, 'recall': 0.8802228412256268, 'f1': 0.8939179632248938, 'number': 1077} | 0.8761 | 0.8569 | 0.8664 | 0.8023 |
| 0.0026 | 63.1579 | 1200 | 1.6588 | {'precision': 0.8784596871239471, 'recall': 0.8935128518971848, 'f1': 0.8859223300970873, 'number': 817} | {'precision': 0.532258064516129, 'recall': 0.5546218487394958, 'f1': 0.54320987654321, 'number': 119} | {'precision': 0.8739946380697051, 'recall': 0.9080779944289693, 'f1': 0.8907103825136613, 'number': 1077} | 0.8554 | 0.8813 | 0.8681 | 0.7971 |
| 0.0013 | 73.6842 | 1400 | 1.6428 | {'precision': 0.903822441430333, 'recall': 0.8971848225214198, 'f1': 0.9004914004914006, 'number': 817} | {'precision': 0.6166666666666667, 'recall': 0.6218487394957983, 'f1': 0.6192468619246863, 'number': 119} | {'precision': 0.9017132551848512, 'recall': 0.9285051067780873, 'f1': 0.9149130832570905, 'number': 1077} | 0.8858 | 0.8977 | 0.8917 | 0.8127 |
| 0.0009 | 84.2105 | 1600 | 1.6516 | {'precision': 0.8909090909090909, 'recall': 0.8996328029375765, 'f1': 0.8952496954933008, 'number': 817} | {'precision': 0.6132075471698113, 'recall': 0.5462184873949579, 'f1': 0.5777777777777778, 'number': 119} | {'precision': 0.9070837166513339, 'recall': 0.9155060352831941, 'f1': 0.911275415896488, 'number': 1077} | 0.8850 | 0.8872 | 0.8861 | 0.8116 |
| 0.0007 | 94.7368 | 1800 | 1.7017 | {'precision': 0.8470319634703196, 'recall': 0.9082007343941249, 'f1': 0.8765505020673362, 'number': 817} | {'precision': 0.6521739130434783, 'recall': 0.5042016806722689, 'f1': 0.5687203791469194, 'number': 119} | {'precision': 0.8938547486033519, 'recall': 0.8913649025069638, 'f1': 0.8926080892608089, 'number': 1077} | 0.8629 | 0.8753 | 0.8691 | 0.8004 |
| 0.0004 | 105.2632 | 2000 | 1.7304 | {'precision': 0.8624708624708625, 'recall': 0.9057527539779682, 'f1': 0.8835820895522388, 'number': 817} | {'precision': 0.6095238095238096, 'recall': 0.5378151260504201, 'f1': 0.5714285714285715, 'number': 119} | {'precision': 0.906046511627907, 'recall': 0.904363974001857, 'f1': 0.9052044609665427, 'number': 1077} | 0.8724 | 0.8833 | 0.8778 | 0.8019 |
| 0.0003 | 115.7895 | 2200 | 1.7230 | {'precision': 0.8723404255319149, 'recall': 0.9033047735618115, 'f1': 0.8875526157546603, 'number': 817} | {'precision': 0.6702127659574468, 'recall': 0.5294117647058824, 'f1': 0.5915492957746479, 'number': 119} | {'precision': 0.8992740471869328, 'recall': 0.9201485608170845, 'f1': 0.9095915557595228, 'number': 1077} | 0.8776 | 0.8902 | 0.8838 | 0.8049 |
| 0.0002 | 126.3158 | 2400 | 1.7177 | {'precision': 0.8729411764705882, 'recall': 0.9082007343941249, 'f1': 0.8902219556088783, 'number': 817} | {'precision': 0.6421052631578947, 'recall': 0.5126050420168067, 'f1': 0.5700934579439252, 'number': 119} | {'precision': 0.906021897810219, 'recall': 0.9220055710306406, 'f1': 0.9139438564196963, 'number': 1077} | 0.8800 | 0.8922 | 0.8860 | 0.8064 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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