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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: 1.4190
- Answer: {'precision': 0.8649592549476135, 'recall': 0.9094247246022031, 'f1': 0.886634844868735, 'number': 817}
- Header: {'precision': 0.6597938144329897, 'recall': 0.5378151260504201, 'f1': 0.5925925925925926, 'number': 119}
- Question: {'precision': 0.8840970350404312, 'recall': 0.9136490250696379, 'f1': 0.8986301369863015, 'number': 1077}
- Overall Precision: 0.8656
- Overall Recall: 0.8897
- Overall F1: 0.8775
- Overall Accuracy: 0.8041
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.4199 | 10.53 | 200 | 1.0303 | {'precision': 0.8458904109589042, 'recall': 0.9069767441860465, 'f1': 0.8753691671588896, 'number': 817} | {'precision': 0.4470588235294118, 'recall': 0.6386554621848739, 'f1': 0.5259515570934256, 'number': 119} | {'precision': 0.8797653958944281, 'recall': 0.8356545961002786, 'f1': 0.8571428571428572, 'number': 1077} | 0.8299 | 0.8530 | 0.8413 | 0.7784 |
| 0.0485 | 21.05 | 400 | 1.3395 | {'precision': 0.8127705627705628, 'recall': 0.9192166462668299, 'f1': 0.8627225732337737, 'number': 817} | {'precision': 0.6463414634146342, 'recall': 0.44537815126050423, 'f1': 0.527363184079602, 'number': 119} | {'precision': 0.8720292504570384, 'recall': 0.8857938718662952, 'f1': 0.878857669276831, 'number': 1077} | 0.8371 | 0.8733 | 0.8549 | 0.7851 |
| 0.0154 | 31.58 | 600 | 1.2980 | {'precision': 0.8578034682080925, 'recall': 0.9082007343941249, 'f1': 0.8822829964328182, 'number': 817} | {'precision': 0.5742574257425742, 'recall': 0.48739495798319327, 'f1': 0.5272727272727273, 'number': 119} | {'precision': 0.87322695035461, 'recall': 0.914577530176416, 'f1': 0.8934240362811792, 'number': 1077} | 0.8524 | 0.8867 | 0.8692 | 0.8145 |
| 0.0076 | 42.11 | 800 | 1.3862 | {'precision': 0.8296703296703297, 'recall': 0.9241126070991432, 'f1': 0.8743485813549509, 'number': 817} | {'precision': 0.6206896551724138, 'recall': 0.453781512605042, 'f1': 0.5242718446601942, 'number': 119} | {'precision': 0.8699472759226714, 'recall': 0.9192200557103064, 'f1': 0.8939051918735892, 'number': 1077} | 0.8426 | 0.8937 | 0.8674 | 0.8016 |
| 0.0055 | 52.63 | 1000 | 1.4190 | {'precision': 0.8649592549476135, 'recall': 0.9094247246022031, 'f1': 0.886634844868735, 'number': 817} | {'precision': 0.6597938144329897, 'recall': 0.5378151260504201, 'f1': 0.5925925925925926, 'number': 119} | {'precision': 0.8840970350404312, 'recall': 0.9136490250696379, 'f1': 0.8986301369863015, 'number': 1077} | 0.8656 | 0.8897 | 0.8775 | 0.8041 |
| 0.0026 | 63.16 | 1200 | 1.5891 | {'precision': 0.8293478260869566, 'recall': 0.9339045287637698, 'f1': 0.8785261945883708, 'number': 817} | {'precision': 0.5922330097087378, 'recall': 0.5126050420168067, 'f1': 0.5495495495495496, 'number': 119} | {'precision': 0.9082217973231358, 'recall': 0.8820798514391829, 'f1': 0.8949599623174752, 'number': 1077} | 0.8574 | 0.8813 | 0.8692 | 0.8058 |
| 0.0027 | 73.68 | 1400 | 1.6258 | {'precision': 0.8331466965285554, 'recall': 0.9106487148102815, 'f1': 0.8701754385964913, 'number': 817} | {'precision': 0.5833333333333334, 'recall': 0.5294117647058824, 'f1': 0.5550660792951542, 'number': 119} | {'precision': 0.8783542039355993, 'recall': 0.9117920148560817, 'f1': 0.894760820045558, 'number': 1077} | 0.8443 | 0.8887 | 0.8659 | 0.7927 |
| 0.0009 | 84.21 | 1600 | 1.6324 | {'precision': 0.8621495327102804, 'recall': 0.9033047735618115, 'f1': 0.8822474596533174, 'number': 817} | {'precision': 0.6, 'recall': 0.5294117647058824, 'f1': 0.5625, 'number': 119} | {'precision': 0.8733153638814016, 'recall': 0.9025069637883009, 'f1': 0.8876712328767123, 'number': 1077} | 0.8549 | 0.8808 | 0.8676 | 0.7950 |
| 0.0007 | 94.74 | 1800 | 1.8058 | {'precision': 0.8278145695364238, 'recall': 0.9179926560587516, 'f1': 0.8705745792222868, 'number': 817} | {'precision': 0.5714285714285714, 'recall': 0.5042016806722689, 'f1': 0.5357142857142857, 'number': 119} | {'precision': 0.9029495718363464, 'recall': 0.8811513463324049, 'f1': 0.8919172932330827, 'number': 1077} | 0.8531 | 0.8738 | 0.8633 | 0.7927 |
| 0.0005 | 105.26 | 2000 | 1.8281 | {'precision': 0.8543799772468714, 'recall': 0.9192166462668299, 'f1': 0.8856132075471698, 'number': 817} | {'precision': 0.5238095238095238, 'recall': 0.5546218487394958, 'f1': 0.5387755102040817, 'number': 119} | {'precision': 0.9050279329608939, 'recall': 0.9025069637883009, 'f1': 0.903765690376569, 'number': 1077} | 0.8605 | 0.8887 | 0.8744 | 0.7915 |
| 0.0004 | 115.79 | 2200 | 1.6623 | {'precision': 0.8643867924528302, 'recall': 0.8971848225214198, 'f1': 0.8804804804804804, 'number': 817} | {'precision': 0.5426356589147286, 'recall': 0.5882352941176471, 'f1': 0.5645161290322581, 'number': 119} | {'precision': 0.8898916967509025, 'recall': 0.9155060352831941, 'f1': 0.9025171624713959, 'number': 1077} | 0.8580 | 0.8887 | 0.8731 | 0.8055 |
| 0.0003 | 126.32 | 2400 | 1.7066 | {'precision': 0.8649592549476135, 'recall': 0.9094247246022031, 'f1': 0.886634844868735, 'number': 817} | {'precision': 0.5689655172413793, 'recall': 0.5546218487394958, 'f1': 0.5617021276595745, 'number': 119} | {'precision': 0.8988970588235294, 'recall': 0.9080779944289693, 'f1': 0.9034642032332563, 'number': 1077} | 0.8662 | 0.8877 | 0.8768 | 0.8010 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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