File size: 7,841 Bytes
8106fbf |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 |
---
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: 1.6502
- Answer: {'precision': 0.8637413394919169, 'recall': 0.9155446756425949, 'f1': 0.888888888888889, 'number': 817}
- Header: {'precision': 0.625, 'recall': 0.5042016806722689, 'f1': 0.5581395348837209, 'number': 119}
- Question: {'precision': 0.8934280639431617, 'recall': 0.9340761374187558, 'f1': 0.9133000453926464, 'number': 1077}
- Overall Precision: 0.8688
- Overall Recall: 0.9011
- Overall F1: 0.8847
- Overall Accuracy: 0.8015
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.4362 | 10.53 | 200 | 0.9773 | {'precision': 0.8193832599118943, 'recall': 0.9106487148102815, 'f1': 0.862608695652174, 'number': 817} | {'precision': 0.6666666666666666, 'recall': 0.3865546218487395, 'f1': 0.4893617021276595, 'number': 119} | {'precision': 0.8725490196078431, 'recall': 0.9090064995357474, 'f1': 0.8904047294224647, 'number': 1077} | 0.8428 | 0.8788 | 0.8604 | 0.7932 |
| 0.0418 | 21.05 | 400 | 1.4204 | {'precision': 0.8056460369163952, 'recall': 0.9082007343941249, 'f1': 0.85385500575374, 'number': 817} | {'precision': 0.5684210526315789, 'recall': 0.453781512605042, 'f1': 0.5046728971962617, 'number': 119} | {'precision': 0.8854262144821264, 'recall': 0.8969359331476323, 'f1': 0.8911439114391144, 'number': 1077} | 0.8363 | 0.8753 | 0.8553 | 0.7870 |
| 0.0118 | 31.58 | 600 | 1.5084 | {'precision': 0.8661137440758294, 'recall': 0.8947368421052632, 'f1': 0.8801926550270921, 'number': 817} | {'precision': 0.5575221238938053, 'recall': 0.5294117647058824, 'f1': 0.543103448275862, 'number': 119} | {'precision': 0.8864864864864865, 'recall': 0.9136490250696379, 'f1': 0.8998628257887517, 'number': 1077} | 0.8602 | 0.8833 | 0.8716 | 0.7938 |
| 0.0116 | 42.11 | 800 | 1.4934 | {'precision': 0.8497109826589595, 'recall': 0.8996328029375765, 'f1': 0.8739595719381689, 'number': 817} | {'precision': 0.6068376068376068, 'recall': 0.5966386554621849, 'f1': 0.6016949152542374, 'number': 119} | {'precision': 0.8835489833641405, 'recall': 0.8876508820798514, 'f1': 0.8855951829550718, 'number': 1077} | 0.8537 | 0.8753 | 0.8644 | 0.7963 |
| 0.0046 | 52.63 | 1000 | 1.6502 | {'precision': 0.8637413394919169, 'recall': 0.9155446756425949, 'f1': 0.888888888888889, 'number': 817} | {'precision': 0.625, 'recall': 0.5042016806722689, 'f1': 0.5581395348837209, 'number': 119} | {'precision': 0.8934280639431617, 'recall': 0.9340761374187558, 'f1': 0.9133000453926464, 'number': 1077} | 0.8688 | 0.9011 | 0.8847 | 0.8015 |
| 0.0025 | 63.16 | 1200 | 1.6009 | {'precision': 0.8503480278422274, 'recall': 0.8971848225214198, 'f1': 0.8731387730792138, 'number': 817} | {'precision': 0.651685393258427, 'recall': 0.48739495798319327, 'f1': 0.5576923076923077, 'number': 119} | {'precision': 0.8716392020815265, 'recall': 0.9331476323119777, 'f1': 0.9013452914798208, 'number': 1077} | 0.8536 | 0.8922 | 0.8725 | 0.8073 |
| 0.0016 | 73.68 | 1400 | 1.6601 | {'precision': 0.8872727272727273, 'recall': 0.8959608323133414, 'f1': 0.8915956151035324, 'number': 817} | {'precision': 0.67, 'recall': 0.5630252100840336, 'f1': 0.6118721461187214, 'number': 119} | {'precision': 0.8820375335120644, 'recall': 0.9164345403899722, 'f1': 0.8989071038251366, 'number': 1077} | 0.8738 | 0.8872 | 0.8805 | 0.7977 |
| 0.0006 | 84.21 | 1600 | 1.6735 | {'precision': 0.8774038461538461, 'recall': 0.8935128518971848, 'f1': 0.8853850818677986, 'number': 817} | {'precision': 0.6636363636363637, 'recall': 0.6134453781512605, 'f1': 0.6375545851528385, 'number': 119} | {'precision': 0.8782452999104745, 'recall': 0.9108635097493036, 'f1': 0.8942570647219691, 'number': 1077} | 0.8664 | 0.8862 | 0.8762 | 0.7997 |
| 0.0006 | 94.74 | 1800 | 1.6672 | {'precision': 0.8755980861244019, 'recall': 0.8959608323133414, 'f1': 0.8856624319419237, 'number': 817} | {'precision': 0.6545454545454545, 'recall': 0.6050420168067226, 'f1': 0.62882096069869, 'number': 119} | {'precision': 0.8800705467372134, 'recall': 0.9266480965645311, 'f1': 0.902758932609679, 'number': 1077} | 0.8663 | 0.8952 | 0.8805 | 0.8010 |
| 0.0004 | 105.26 | 2000 | 1.6652 | {'precision': 0.8880866425992779, 'recall': 0.9033047735618115, 'f1': 0.895631067961165, 'number': 817} | {'precision': 0.6086956521739131, 'recall': 0.5882352941176471, 'f1': 0.5982905982905983, 'number': 119} | {'precision': 0.8776785714285714, 'recall': 0.9127205199628597, 'f1': 0.8948566226672735, 'number': 1077} | 0.8669 | 0.8897 | 0.8782 | 0.8059 |
| 0.0004 | 115.79 | 2200 | 1.6698 | {'precision': 0.8993865030674847, 'recall': 0.8971848225214198, 'f1': 0.8982843137254903, 'number': 817} | {'precision': 0.631578947368421, 'recall': 0.6050420168067226, 'f1': 0.6180257510729613, 'number': 119} | {'precision': 0.8808243727598566, 'recall': 0.9127205199628597, 'f1': 0.8964888280893752, 'number': 1077} | 0.8743 | 0.8882 | 0.8812 | 0.8096 |
| 0.0002 | 126.32 | 2400 | 1.7190 | {'precision': 0.8888888888888888, 'recall': 0.9008567931456548, 'f1': 0.8948328267477204, 'number': 817} | {'precision': 0.6542056074766355, 'recall': 0.5882352941176471, 'f1': 0.6194690265486726, 'number': 119} | {'precision': 0.8815672306322351, 'recall': 0.9192200557103064, 'f1': 0.9, 'number': 1077} | 0.8727 | 0.8922 | 0.8823 | 0.8045 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|