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---
license: mit
tags:
- generated_from_trainer
datasets:
- funsd-layoutlmv3
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 the funsd-layoutlmv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6055
- Answer: {'precision': 0.8728323699421965, 'recall': 0.9241126070991432, 'f1': 0.8977407847800237, 'number': 817}
- Header: {'precision': 0.6, 'recall': 0.5042016806722689, 'f1': 0.547945205479452, 'number': 119}
- Question: {'precision': 0.9, 'recall': 0.9108635097493036, 'f1': 0.9053991693585604, 'number': 1077}
- Overall Precision: 0.8740
- Overall Recall: 0.8922
- Overall F1: 0.8830
- Overall Accuracy: 0.8022
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.4096 | 10.53 | 200 | 0.9947 | {'precision': 0.8293515358361775, 'recall': 0.8922888616891065, 'f1': 0.8596698113207547, 'number': 817} | {'precision': 0.5833333333333334, 'recall': 0.5294117647058824, 'f1': 0.5550660792951542, 'number': 119} | {'precision': 0.8858195211786372, 'recall': 0.89322191272052, 'f1': 0.8895053166897827, 'number': 1077} | 0.8461 | 0.8713 | 0.8585 | 0.8133 |
| 0.0451 | 21.05 | 400 | 1.4850 | {'precision': 0.8394495412844036, 'recall': 0.8959608323133414, 'f1': 0.866785079928952, 'number': 817} | {'precision': 0.6333333333333333, 'recall': 0.4789915966386555, 'f1': 0.5454545454545454, 'number': 119} | {'precision': 0.9026974951830443, 'recall': 0.8700092850510678, 'f1': 0.8860520094562647, 'number': 1077} | 0.863 | 0.8574 | 0.8602 | 0.7910 |
| 0.0147 | 31.58 | 600 | 1.5603 | {'precision': 0.8478260869565217, 'recall': 0.9069767441860465, 'f1': 0.8764044943820224, 'number': 817} | {'precision': 0.6336633663366337, 'recall': 0.5378151260504201, 'f1': 0.5818181818181819, 'number': 119} | {'precision': 0.8845807033363391, 'recall': 0.9108635097493036, 'f1': 0.8975297346752059, 'number': 1077} | 0.8570 | 0.8872 | 0.8719 | 0.7948 |
| 0.0077 | 42.11 | 800 | 1.7433 | {'precision': 0.8344444444444444, 'recall': 0.9192166462668299, 'f1': 0.8747815958066395, 'number': 817} | {'precision': 0.5596330275229358, 'recall': 0.5126050420168067, 'f1': 0.5350877192982455, 'number': 119} | {'precision': 0.9031954887218046, 'recall': 0.8922934076137419, 'f1': 0.897711349836525, 'number': 1077} | 0.8553 | 0.8808 | 0.8678 | 0.7910 |
| 0.004 | 52.63 | 1000 | 1.6055 | {'precision': 0.8728323699421965, 'recall': 0.9241126070991432, 'f1': 0.8977407847800237, 'number': 817} | {'precision': 0.6, 'recall': 0.5042016806722689, 'f1': 0.547945205479452, 'number': 119} | {'precision': 0.9, 'recall': 0.9108635097493036, 'f1': 0.9053991693585604, 'number': 1077} | 0.8740 | 0.8922 | 0.8830 | 0.8022 |
| 0.0021 | 63.16 | 1200 | 1.7317 | {'precision': 0.8688524590163934, 'recall': 0.9082007343941249, 'f1': 0.8880909634949131, 'number': 817} | {'precision': 0.5904761904761905, 'recall': 0.5210084033613446, 'f1': 0.5535714285714286, 'number': 119} | {'precision': 0.8847184986595175, 'recall': 0.9192200557103064, 'f1': 0.9016393442622952, 'number': 1077} | 0.8633 | 0.8912 | 0.8770 | 0.7979 |
| 0.0017 | 73.68 | 1400 | 1.7249 | {'precision': 0.8705463182897862, 'recall': 0.8971848225214198, 'f1': 0.8836648583484027, 'number': 817} | {'precision': 0.5555555555555556, 'recall': 0.5042016806722689, 'f1': 0.5286343612334802, 'number': 119} | {'precision': 0.8818181818181818, 'recall': 0.9006499535747446, 'f1': 0.891134588883785, 'number': 1077} | 0.86 | 0.8758 | 0.8678 | 0.8014 |
| 0.0012 | 84.21 | 1600 | 1.8716 | {'precision': 0.8679678530424799, 'recall': 0.9253365973072215, 'f1': 0.8957345971563981, 'number': 817} | {'precision': 0.5652173913043478, 'recall': 0.5462184873949579, 'f1': 0.5555555555555555, 'number': 119} | {'precision': 0.898320895522388, 'recall': 0.8941504178272981, 'f1': 0.8962308050255934, 'number': 1077} | 0.8669 | 0.8862 | 0.8764 | 0.7946 |
| 0.0006 | 94.74 | 1800 | 1.8381 | {'precision': 0.8563218390804598, 'recall': 0.9118727050183598, 'f1': 0.8832246591582691, 'number': 817} | {'precision': 0.6055045871559633, 'recall': 0.5546218487394958, 'f1': 0.5789473684210525, 'number': 119} | {'precision': 0.8931226765799256, 'recall': 0.8922934076137419, 'f1': 0.8927078495123084, 'number': 1077} | 0.8623 | 0.8803 | 0.8712 | 0.7954 |
| 0.0007 | 105.26 | 2000 | 1.7090 | {'precision': 0.8822115384615384, 'recall': 0.8984088127294981, 'f1': 0.8902365069739235, 'number': 817} | {'precision': 0.576271186440678, 'recall': 0.5714285714285714, 'f1': 0.5738396624472574, 'number': 119} | {'precision': 0.8811881188118812, 'recall': 0.9090064995357474, 'f1': 0.8948811700182815, 'number': 1077} | 0.8641 | 0.8847 | 0.8743 | 0.8122 |
| 0.0003 | 115.79 | 2200 | 1.7487 | {'precision': 0.8730723606168446, 'recall': 0.9008567931456548, 'f1': 0.8867469879518072, 'number': 817} | {'precision': 0.5645161290322581, 'recall': 0.5882352941176471, 'f1': 0.5761316872427984, 'number': 119} | {'precision': 0.8936755270394133, 'recall': 0.9052924791086351, 'f1': 0.8994464944649446, 'number': 1077} | 0.8654 | 0.8847 | 0.8750 | 0.8084 |
| 0.0002 | 126.32 | 2400 | 1.7644 | {'precision': 0.8686046511627907, 'recall': 0.9143206854345165, 'f1': 0.8908765652951699, 'number': 817} | {'precision': 0.5932203389830508, 'recall': 0.5882352941176471, 'f1': 0.5907172995780592, 'number': 119} | {'precision': 0.8961397058823529, 'recall': 0.9052924791086351, 'f1': 0.9006928406466513, 'number': 1077} | 0.8674 | 0.8902 | 0.8786 | 0.8091 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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