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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: 1.5687
- Answer: {'precision': 0.8635294117647059, 'recall': 0.8984088127294981, 'f1': 0.8806238752249551, 'number': 817}
- Header: {'precision': 0.6781609195402298, 'recall': 0.4957983193277311, 'f1': 0.5728155339805825, 'number': 119}
- Question: {'precision': 0.8912655971479501, 'recall': 0.9285051067780873, 'f1': 0.9095043201455207, 'number': 1077}
- Overall Precision: 0.8708
- Overall Recall: 0.8907
- Overall F1: 0.8806
- Overall Accuracy: 0.8098
## 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 |
|:-------------:|:--------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.4119 | 10.5263 | 200 | 1.0913 | {'precision': 0.8155555555555556, 'recall': 0.8984088127294981, 'f1': 0.8549796156086196, 'number': 817} | {'precision': 0.5523809523809524, 'recall': 0.48739495798319327, 'f1': 0.5178571428571428, 'number': 119} | {'precision': 0.8904363974001857, 'recall': 0.8904363974001857, 'f1': 0.8904363974001857, 'number': 1077} | 0.8410 | 0.8698 | 0.8552 | 0.7742 |
| 0.0512 | 21.0526 | 400 | 1.3167 | {'precision': 0.8363636363636363, 'recall': 0.9008567931456548, 'f1': 0.8674130819092517, 'number': 817} | {'precision': 0.6160714285714286, 'recall': 0.5798319327731093, 'f1': 0.5974025974025975, 'number': 119} | {'precision': 0.8839122486288848, 'recall': 0.8978644382544104, 'f1': 0.8908337171810224, 'number': 1077} | 0.8495 | 0.8803 | 0.8646 | 0.7919 |
| 0.0159 | 31.5789 | 600 | 1.4418 | {'precision': 0.8539458186101295, 'recall': 0.8873929008567931, 'f1': 0.8703481392557022, 'number': 817} | {'precision': 0.5913978494623656, 'recall': 0.46218487394957986, 'f1': 0.5188679245283019, 'number': 119} | {'precision': 0.8617113223854796, 'recall': 0.9257195914577531, 'f1': 0.8925693822739481, 'number': 1077} | 0.8466 | 0.8828 | 0.8643 | 0.7994 |
| 0.0085 | 42.1053 | 800 | 1.3252 | {'precision': 0.8408839779005525, 'recall': 0.9314565483476133, 'f1': 0.8838559814169571, 'number': 817} | {'precision': 0.6176470588235294, 'recall': 0.5294117647058824, 'f1': 0.5701357466063349, 'number': 119} | {'precision': 0.8946412352406903, 'recall': 0.914577530176416, 'f1': 0.9044995408631772, 'number': 1077} | 0.8582 | 0.8987 | 0.8779 | 0.8077 |
| 0.0035 | 52.6316 | 1000 | 1.4334 | {'precision': 0.8364269141531323, 'recall': 0.8824969400244798, 'f1': 0.8588445503275759, 'number': 817} | {'precision': 0.6, 'recall': 0.5798319327731093, 'f1': 0.5897435897435898, 'number': 119} | {'precision': 0.8983516483516484, 'recall': 0.9108635097493036, 'f1': 0.9045643153526972, 'number': 1077} | 0.8560 | 0.8798 | 0.8677 | 0.7988 |
| 0.0031 | 63.1579 | 1200 | 1.3464 | {'precision': 0.8569780853517878, 'recall': 0.9094247246022031, 'f1': 0.8824228028503563, 'number': 817} | {'precision': 0.6836734693877551, 'recall': 0.5630252100840336, 'f1': 0.6175115207373272, 'number': 119} | {'precision': 0.8909090909090909, 'recall': 0.9099350046425255, 'f1': 0.90032154340836, 'number': 1077} | 0.8668 | 0.8892 | 0.8779 | 0.8164 |
| 0.0017 | 73.6842 | 1400 | 1.5019 | {'precision': 0.883054892601432, 'recall': 0.9057527539779682, 'f1': 0.8942598187311178, 'number': 817} | {'precision': 0.6565656565656566, 'recall': 0.5462184873949579, 'f1': 0.5963302752293578, 'number': 119} | {'precision': 0.8922528940338379, 'recall': 0.9303621169916435, 'f1': 0.9109090909090909, 'number': 1077} | 0.8772 | 0.8977 | 0.8873 | 0.8110 |
| 0.0008 | 84.2105 | 1600 | 1.5955 | {'precision': 0.8760631834750912, 'recall': 0.8824969400244798, 'f1': 0.8792682926829267, 'number': 817} | {'precision': 0.5925925925925926, 'recall': 0.5378151260504201, 'f1': 0.5638766519823789, 'number': 119} | {'precision': 0.8853046594982079, 'recall': 0.9173630454967502, 'f1': 0.9010487916096672, 'number': 1077} | 0.8661 | 0.8808 | 0.8734 | 0.8084 |
| 0.0006 | 94.7368 | 1800 | 1.5931 | {'precision': 0.8713592233009708, 'recall': 0.8788249694002448, 'f1': 0.8750761730652041, 'number': 817} | {'precision': 0.6122448979591837, 'recall': 0.5042016806722689, 'f1': 0.5529953917050692, 'number': 119} | {'precision': 0.8834519572953736, 'recall': 0.9220055710306406, 'f1': 0.9023171285779192, 'number': 1077} | 0.8656 | 0.8798 | 0.8726 | 0.7973 |
| 0.0006 | 105.2632 | 2000 | 1.5735 | {'precision': 0.8676122931442081, 'recall': 0.8984088127294981, 'f1': 0.8827420324714371, 'number': 817} | {'precision': 0.6176470588235294, 'recall': 0.5294117647058824, 'f1': 0.5701357466063349, 'number': 119} | {'precision': 0.8862222222222222, 'recall': 0.9257195914577531, 'f1': 0.9055404178019981, 'number': 1077} | 0.8654 | 0.8912 | 0.8781 | 0.8002 |
| 0.001 | 115.7895 | 2200 | 1.5605 | {'precision': 0.8543352601156069, 'recall': 0.9045287637698899, 'f1': 0.8787158145065399, 'number': 817} | {'precision': 0.6559139784946236, 'recall': 0.5126050420168067, 'f1': 0.5754716981132076, 'number': 119} | {'precision': 0.8935978358881875, 'recall': 0.9201485608170845, 'f1': 0.9066788655077767, 'number': 1077} | 0.8665 | 0.8897 | 0.8779 | 0.8073 |
| 0.0004 | 126.3158 | 2400 | 1.5687 | {'precision': 0.8635294117647059, 'recall': 0.8984088127294981, 'f1': 0.8806238752249551, 'number': 817} | {'precision': 0.6781609195402298, 'recall': 0.4957983193277311, 'f1': 0.5728155339805825, 'number': 119} | {'precision': 0.8912655971479501, 'recall': 0.9285051067780873, 'f1': 0.9095043201455207, 'number': 1077} | 0.8708 | 0.8907 | 0.8806 | 0.8098 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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