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---
library_name: transformers
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: 1.7319
- Answer: {'precision': 0.8804733727810651, 'recall': 0.9106487148102815, 'f1': 0.8953068592057761, 'number': 817}
- Header: {'precision': 0.6016949152542372, 'recall': 0.5966386554621849, 'f1': 0.5991561181434599, 'number': 119}
- Question: {'precision': 0.9095106186518929, 'recall': 0.914577530176416, 'f1': 0.912037037037037, 'number': 1077}
- Overall Precision: 0.8798
- Overall Recall: 0.8942
- Overall F1: 0.8869
- Overall Accuracy: 0.8046
## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- 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 |
|:-------------:|:--------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.4066 | 10.5263 | 200 | 1.0212 | {'precision': 0.8098434004474273, 'recall': 0.8861689106487148, 'f1': 0.8462887200467561, 'number': 817} | {'precision': 0.5106382978723404, 'recall': 0.6050420168067226, 'f1': 0.5538461538461538, 'number': 119} | {'precision': 0.8872180451127819, 'recall': 0.8765088207985144, 'f1': 0.88183092013078, 'number': 1077} | 0.8290 | 0.8644 | 0.8463 | 0.7855 |
| 0.0465 | 21.0526 | 400 | 1.4003 | {'precision': 0.8215859030837004, 'recall': 0.9130966952264382, 'f1': 0.864927536231884, 'number': 817} | {'precision': 0.5794392523364486, 'recall': 0.5210084033613446, 'f1': 0.5486725663716815, 'number': 119} | {'precision': 0.8856624319419237, 'recall': 0.9062209842154132, 'f1': 0.895823772372648, 'number': 1077} | 0.8427 | 0.8862 | 0.8639 | 0.7900 |
| 0.0143 | 31.5789 | 600 | 1.5416 | {'precision': 0.8493150684931506, 'recall': 0.9106487148102815, 'f1': 0.8789131718842291, 'number': 817} | {'precision': 0.5769230769230769, 'recall': 0.5042016806722689, 'f1': 0.5381165919282511, 'number': 119} | {'precision': 0.8916967509025271, 'recall': 0.9173630454967502, 'f1': 0.9043478260869565, 'number': 1077} | 0.8582 | 0.8902 | 0.8739 | 0.7835 |
| 0.0067 | 42.1053 | 800 | 1.5372 | {'precision': 0.8668252080856124, 'recall': 0.8922888616891065, 'f1': 0.879372738238842, 'number': 817} | {'precision': 0.5855855855855856, 'recall': 0.5462184873949579, 'f1': 0.5652173913043478, 'number': 119} | {'precision': 0.8869801084990958, 'recall': 0.9108635097493036, 'f1': 0.8987631699496106, 'number': 1077} | 0.8625 | 0.8818 | 0.8720 | 0.7937 |
| 0.0051 | 52.6316 | 1000 | 1.5657 | {'precision': 0.8652912621359223, 'recall': 0.8727050183598531, 'f1': 0.8689823278488727, 'number': 817} | {'precision': 0.5867768595041323, 'recall': 0.5966386554621849, 'f1': 0.5916666666666667, 'number': 119} | {'precision': 0.8840321141837645, 'recall': 0.9201485608170845, 'f1': 0.9017288444040036, 'number': 1077} | 0.8591 | 0.8818 | 0.8703 | 0.7988 |
| 0.0031 | 63.1579 | 1200 | 1.6563 | {'precision': 0.8412698412698413, 'recall': 0.9082007343941249, 'f1': 0.8734549735138316, 'number': 817} | {'precision': 0.6195652173913043, 'recall': 0.4789915966386555, 'f1': 0.5402843601895735, 'number': 119} | {'precision': 0.8938700823421775, 'recall': 0.9071494893221913, 'f1': 0.9004608294930875, 'number': 1077} | 0.8592 | 0.8823 | 0.8706 | 0.7973 |
| 0.0012 | 73.6842 | 1400 | 1.6712 | {'precision': 0.8588374851720048, 'recall': 0.8861689106487148, 'f1': 0.8722891566265061, 'number': 817} | {'precision': 0.6063829787234043, 'recall': 0.4789915966386555, 'f1': 0.5352112676056338, 'number': 119} | {'precision': 0.8782608695652174, 'recall': 0.9377901578458682, 'f1': 0.9070498428378985, 'number': 1077} | 0.8582 | 0.8897 | 0.8737 | 0.8013 |
| 0.0011 | 84.2105 | 1600 | 1.6732 | {'precision': 0.8719153936545241, 'recall': 0.9082007343941249, 'f1': 0.8896882494004795, 'number': 817} | {'precision': 0.6019417475728155, 'recall': 0.5210084033613446, 'f1': 0.5585585585585585, 'number': 119} | {'precision': 0.8968609865470852, 'recall': 0.9285051067780873, 'f1': 0.9124087591240877, 'number': 1077} | 0.8719 | 0.8962 | 0.8839 | 0.8089 |
| 0.0009 | 94.7368 | 1800 | 1.6677 | {'precision': 0.875, 'recall': 0.9082007343941249, 'f1': 0.8912912912912913, 'number': 817} | {'precision': 0.6116504854368932, 'recall': 0.5294117647058824, 'f1': 0.5675675675675675, 'number': 119} | {'precision': 0.8976449275362319, 'recall': 0.9201485608170845, 'f1': 0.9087574507106833, 'number': 1077} | 0.8740 | 0.8922 | 0.8830 | 0.8126 |
| 0.0004 | 105.2632 | 2000 | 1.7264 | {'precision': 0.8803317535545023, 'recall': 0.9094247246022031, 'f1': 0.8946417820590005, 'number': 817} | {'precision': 0.6226415094339622, 'recall': 0.5546218487394958, 'f1': 0.5866666666666668, 'number': 119} | {'precision': 0.9101741521539871, 'recall': 0.9220055710306406, 'f1': 0.9160516605166051, 'number': 1077} | 0.8829 | 0.8952 | 0.8890 | 0.8074 |
| 0.0003 | 115.7895 | 2200 | 1.7219 | {'precision': 0.8800959232613909, 'recall': 0.8984088127294981, 'f1': 0.8891580860084797, 'number': 817} | {'precision': 0.625, 'recall': 0.5882352941176471, 'f1': 0.6060606060606061, 'number': 119} | {'precision': 0.8974820143884892, 'recall': 0.9266480965645311, 'f1': 0.9118318867062585, 'number': 1077} | 0.8756 | 0.8952 | 0.8853 | 0.8082 |
| 0.0002 | 126.3158 | 2400 | 1.7319 | {'precision': 0.8804733727810651, 'recall': 0.9106487148102815, 'f1': 0.8953068592057761, 'number': 817} | {'precision': 0.6016949152542372, 'recall': 0.5966386554621849, 'f1': 0.5991561181434599, 'number': 119} | {'precision': 0.9095106186518929, 'recall': 0.914577530176416, 'f1': 0.912037037037037, 'number': 1077} | 0.8798 | 0.8942 | 0.8869 | 0.8046 |
### Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
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