---
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: 1.5849
- Answer: {'precision': 0.8488636363636364, 'recall': 0.9143206854345165, 'f1': 0.8803771361225692, 'number': 817}
- Header: {'precision': 0.5631067961165048, 'recall': 0.48739495798319327, 'f1': 0.5225225225225225, 'number': 119}
- Question: {'precision': 0.8887859128822985, 'recall': 0.8904363974001857, 'f1': 0.8896103896103896, 'number': 1077}
- Overall Precision: 0.8555
- Overall Recall: 0.8763
- Overall F1: 0.8658
- Overall Accuracy: 0.8017

## 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: 2
- eval_batch_size: 2
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.7834        | 2.67  | 200  | 0.5897          | {'precision': 0.8059536934950385, 'recall': 0.8947368421052632, 'f1': 0.8480278422273781, 'number': 817} | {'precision': 0.4069767441860465, 'recall': 0.29411764705882354, 'f1': 0.34146341463414637, 'number': 119}  | {'precision': 0.8141447368421053, 'recall': 0.9192200557103064, 'f1': 0.8634976013955518, 'number': 1077} | 0.7949            | 0.8723         | 0.8318     | 0.7937           |
| 0.2848        | 5.33  | 400  | 0.8788          | {'precision': 0.8155136268343816, 'recall': 0.9522643818849449, 'f1': 0.8785996612083569, 'number': 817} | {'precision': 0.46218487394957986, 'recall': 0.46218487394957986, 'f1': 0.46218487394957986, 'number': 119} | {'precision': 0.8724584103512015, 'recall': 0.8765088207985144, 'f1': 0.8744789254284391, 'number': 1077} | 0.8246            | 0.8828         | 0.8527     | 0.7916           |
| 0.1446        | 8.0   | 600  | 1.0402          | {'precision': 0.8363844393592678, 'recall': 0.8947368421052632, 'f1': 0.8645771732702543, 'number': 817} | {'precision': 0.5894736842105263, 'recall': 0.47058823529411764, 'f1': 0.5233644859813084, 'number': 119}   | {'precision': 0.8886861313868614, 'recall': 0.904363974001857, 'f1': 0.8964565117349288, 'number': 1077}  | 0.8528            | 0.8748         | 0.8637     | 0.7891           |
| 0.0791        | 10.67 | 800  | 1.1878          | {'precision': 0.8659549228944247, 'recall': 0.8935128518971848, 'f1': 0.8795180722891566, 'number': 817} | {'precision': 0.48360655737704916, 'recall': 0.4957983193277311, 'f1': 0.4896265560165975, 'number': 119}   | {'precision': 0.8647007805724197, 'recall': 0.9257195914577531, 'f1': 0.8941704035874439, 'number': 1077} | 0.8432            | 0.8872         | 0.8647     | 0.7973           |
| 0.0459        | 13.33 | 1000 | 1.3884          | {'precision': 0.8078175895765473, 'recall': 0.9106487148102815, 'f1': 0.856156501726122, 'number': 817}  | {'precision': 0.5789473684210527, 'recall': 0.46218487394957986, 'f1': 0.514018691588785, 'number': 119}    | {'precision': 0.8819702602230484, 'recall': 0.8811513463324049, 'f1': 0.8815606130980028, 'number': 1077} | 0.8356            | 0.8684         | 0.8516     | 0.7831           |
| 0.0278        | 16.0  | 1200 | 1.4707          | {'precision': 0.8415051311288484, 'recall': 0.9033047735618115, 'f1': 0.8713105076741442, 'number': 817} | {'precision': 0.5869565217391305, 'recall': 0.453781512605042, 'f1': 0.5118483412322274, 'number': 119}     | {'precision': 0.8690685413005272, 'recall': 0.9182915506035283, 'f1': 0.8930022573363431, 'number': 1077} | 0.8453            | 0.8847         | 0.8646     | 0.7801           |
| 0.0105        | 18.67 | 1400 | 1.5749          | {'precision': 0.843069873997709, 'recall': 0.9008567931456548, 'f1': 0.8710059171597634, 'number': 817}  | {'precision': 0.5426356589147286, 'recall': 0.5882352941176471, 'f1': 0.5645161290322581, 'number': 119}    | {'precision': 0.8799630655586335, 'recall': 0.8848653667595172, 'f1': 0.8824074074074075, 'number': 1077} | 0.8436            | 0.8738         | 0.8585     | 0.7800           |
| 0.0083        | 21.33 | 1600 | 1.5530          | {'precision': 0.8551236749116607, 'recall': 0.8886168910648715, 'f1': 0.8715486194477792, 'number': 817} | {'precision': 0.5727272727272728, 'recall': 0.5294117647058824, 'f1': 0.5502183406113538, 'number': 119}    | {'precision': 0.8669032830523514, 'recall': 0.9071494893221913, 'f1': 0.8865698729582577, 'number': 1077} | 0.8466            | 0.8773         | 0.8617     | 0.8007           |
| 0.0045        | 24.0  | 1800 | 1.5849          | {'precision': 0.8488636363636364, 'recall': 0.9143206854345165, 'f1': 0.8803771361225692, 'number': 817} | {'precision': 0.5631067961165048, 'recall': 0.48739495798319327, 'f1': 0.5225225225225225, 'number': 119}   | {'precision': 0.8887859128822985, 'recall': 0.8904363974001857, 'f1': 0.8896103896103896, 'number': 1077} | 0.8555            | 0.8763         | 0.8658     | 0.8017           |
| 0.0025        | 26.67 | 2000 | 1.6119          | {'precision': 0.8464203233256351, 'recall': 0.8971848225214198, 'f1': 0.8710635769459298, 'number': 817} | {'precision': 0.5566037735849056, 'recall': 0.4957983193277311, 'f1': 0.5244444444444444, 'number': 119}    | {'precision': 0.8709386281588448, 'recall': 0.8960074280408542, 'f1': 0.8832951945080092, 'number': 1077} | 0.8447            | 0.8728         | 0.8585     | 0.7914           |
| 0.0019        | 29.33 | 2200 | 1.5579          | {'precision': 0.8661971830985915, 'recall': 0.9033047735618115, 'f1': 0.884361893349311, 'number': 817}  | {'precision': 0.5660377358490566, 'recall': 0.5042016806722689, 'f1': 0.5333333333333334, 'number': 119}    | {'precision': 0.8654188948306596, 'recall': 0.9015784586815228, 'f1': 0.8831286948613006, 'number': 1077} | 0.8505            | 0.8788         | 0.8644     | 0.7971           |
| 0.0008        | 32.0  | 2400 | 1.5983          | {'precision': 0.8530424799081515, 'recall': 0.9094247246022031, 'f1': 0.8803317535545023, 'number': 817} | {'precision': 0.5714285714285714, 'recall': 0.5042016806722689, 'f1': 0.5357142857142857, 'number': 119}    | {'precision': 0.8768248175182481, 'recall': 0.8922934076137419, 'f1': 0.8844914864242981, 'number': 1077} | 0.8514            | 0.8763         | 0.8636     | 0.7982           |


### Framework versions

- Transformers 4.30.0.dev0
- Pytorch 1.8.0+cu101
- Datasets 2.12.0
- Tokenizers 0.13.3