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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: 1.6681
- Answer: {'precision': 0.8778173190984578, 'recall': 0.9057527539779682, 'f1': 0.891566265060241, 'number': 817}
- Header: {'precision': 0.6036036036036037, 'recall': 0.5630252100840336, 'f1': 0.582608695652174, 'number': 119}
- Question: {'precision': 0.9024839006439742, 'recall': 0.9108635097493036, 'f1': 0.9066543438077633, 'number': 1077}
- Overall Precision: 0.8760
- Overall Recall: 0.8882
- Overall F1: 0.8821
- Overall Accuracy: 0.8030
## 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 |
|:-------------:|:--------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.431 | 10.5263 | 200 | 1.0598 | {'precision': 0.8017718715393134, 'recall': 0.8861689106487148, 'f1': 0.841860465116279, 'number': 817} | {'precision': 0.4228187919463087, 'recall': 0.5294117647058824, 'f1': 0.47014925373134325, 'number': 119} | {'precision': 0.8755935422602089, 'recall': 0.8560817084493965, 'f1': 0.8657276995305164, 'number': 1077} | 0.8119 | 0.8490 | 0.8300 | 0.7774 |
| 0.0542 | 21.0526 | 400 | 1.2173 | {'precision': 0.8382352941176471, 'recall': 0.9069767441860465, 'f1': 0.8712522045855379, 'number': 817} | {'precision': 0.5350877192982456, 'recall': 0.5126050420168067, 'f1': 0.5236051502145922, 'number': 119} | {'precision': 0.8882521489971347, 'recall': 0.8635097493036211, 'f1': 0.8757062146892656, 'number': 1077} | 0.8469 | 0.8604 | 0.8536 | 0.8016 |
| 0.014 | 31.5789 | 600 | 1.2955 | {'precision': 0.8415051311288484, 'recall': 0.9033047735618115, 'f1': 0.8713105076741442, 'number': 817} | {'precision': 0.6210526315789474, 'recall': 0.4957983193277311, 'f1': 0.5514018691588785, 'number': 119} | {'precision': 0.8972477064220183, 'recall': 0.9080779944289693, 'f1': 0.9026303645592985, 'number': 1077} | 0.8608 | 0.8818 | 0.8712 | 0.8160 |
| 0.0064 | 42.1053 | 800 | 1.2848 | {'precision': 0.8696186961869619, 'recall': 0.8653610771113831, 'f1': 0.8674846625766871, 'number': 817} | {'precision': 0.5193798449612403, 'recall': 0.5630252100840336, 'f1': 0.5403225806451614, 'number': 119} | {'precision': 0.858274647887324, 'recall': 0.9052924791086351, 'f1': 0.8811568007230005, 'number': 1077} | 0.8417 | 0.8689 | 0.8550 | 0.8222 |
| 0.0037 | 52.6316 | 1000 | 1.5983 | {'precision': 0.8530751708428246, 'recall': 0.9167686658506732, 'f1': 0.8837758112094395, 'number': 817} | {'precision': 0.5658914728682171, 'recall': 0.6134453781512605, 'f1': 0.5887096774193549, 'number': 119} | {'precision': 0.8946360153256705, 'recall': 0.8672237697307336, 'f1': 0.8807166430928807, 'number': 1077} | 0.8562 | 0.8723 | 0.8642 | 0.7916 |
| 0.0034 | 63.1579 | 1200 | 1.5936 | {'precision': 0.85, 'recall': 0.9155446756425949, 'f1': 0.881555686505598, 'number': 817} | {'precision': 0.5619047619047619, 'recall': 0.4957983193277311, 'f1': 0.5267857142857143, 'number': 119} | {'precision': 0.8912442396313364, 'recall': 0.8978644382544104, 'f1': 0.8945420906567992, 'number': 1077} | 0.8570 | 0.8813 | 0.8690 | 0.8102 |
| 0.0021 | 73.6842 | 1400 | 1.4765 | {'precision': 0.8558139534883721, 'recall': 0.9008567931456548, 'f1': 0.877757901013715, 'number': 817} | {'precision': 0.5619047619047619, 'recall': 0.4957983193277311, 'f1': 0.5267857142857143, 'number': 119} | {'precision': 0.885036496350365, 'recall': 0.9006499535747446, 'f1': 0.892774965485504, 'number': 1077} | 0.8564 | 0.8768 | 0.8665 | 0.8010 |
| 0.0009 | 84.2105 | 1600 | 1.6681 | {'precision': 0.8778173190984578, 'recall': 0.9057527539779682, 'f1': 0.891566265060241, 'number': 817} | {'precision': 0.6036036036036037, 'recall': 0.5630252100840336, 'f1': 0.582608695652174, 'number': 119} | {'precision': 0.9024839006439742, 'recall': 0.9108635097493036, 'f1': 0.9066543438077633, 'number': 1077} | 0.8760 | 0.8882 | 0.8821 | 0.8030 |
| 0.0003 | 94.7368 | 1800 | 1.6379 | {'precision': 0.8595238095238096, 'recall': 0.8837209302325582, 'f1': 0.8714544357272178, 'number': 817} | {'precision': 0.5929203539823009, 'recall': 0.5630252100840336, 'f1': 0.5775862068965517, 'number': 119} | {'precision': 0.896709323583181, 'recall': 0.9108635097493036, 'f1': 0.9037309995393827, 'number': 1077} | 0.8647 | 0.8793 | 0.8719 | 0.7986 |
| 0.0002 | 105.2632 | 2000 | 1.7186 | {'precision': 0.8644859813084113, 'recall': 0.9057527539779682, 'f1': 0.8846383741781233, 'number': 817} | {'precision': 0.5675675675675675, 'recall': 0.5294117647058824, 'f1': 0.5478260869565218, 'number': 119} | {'precision': 0.8921658986175115, 'recall': 0.8987929433611885, 'f1': 0.8954671600370029, 'number': 1077} | 0.8631 | 0.8798 | 0.8713 | 0.7978 |
| 0.0003 | 115.7895 | 2200 | 1.6765 | {'precision': 0.8690476190476191, 'recall': 0.8935128518971848, 'f1': 0.8811104405552203, 'number': 817} | {'precision': 0.5726495726495726, 'recall': 0.5630252100840336, 'f1': 0.5677966101694915, 'number': 119} | {'precision': 0.8934802571166207, 'recall': 0.903435468895079, 'f1': 0.8984302862419206, 'number': 1077} | 0.8651 | 0.8793 | 0.8721 | 0.8000 |
| 0.0003 | 126.3158 | 2400 | 1.7309 | {'precision': 0.8817852834740652, 'recall': 0.8947368421052632, 'f1': 0.888213851761847, 'number': 817} | {'precision': 0.5675675675675675, 'recall': 0.5294117647058824, 'f1': 0.5478260869565218, 'number': 119} | {'precision': 0.8914233576642335, 'recall': 0.9071494893221913, 'f1': 0.8992176714219972, 'number': 1077} | 0.8698 | 0.8798 | 0.8748 | 0.7959 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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