File size: 7,815 Bytes
cd51126 |
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 |
---
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: 1.6122
- Answer: {'precision': 0.8905882352941177, 'recall': 0.9265605875152999, 'f1': 0.9082183563287344, 'number': 817}
- Header: {'precision': 0.6379310344827587, 'recall': 0.6218487394957983, 'f1': 0.6297872340425532, 'number': 119}
- Question: {'precision': 0.8964252978918423, 'recall': 0.9080779944289693, 'f1': 0.9022140221402215, 'number': 1077}
- Overall Precision: 0.8794
- Overall Recall: 0.8987
- Overall F1: 0.8889
- Overall Accuracy: 0.8020
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.4109 | 10.53 | 200 | 0.9316 | {'precision': 0.8169491525423729, 'recall': 0.8849449204406364, 'f1': 0.8495887191539365, 'number': 817} | {'precision': 0.5258620689655172, 'recall': 0.5126050420168067, 'f1': 0.5191489361702126, 'number': 119} | {'precision': 0.8695652173913043, 'recall': 0.8913649025069638, 'f1': 0.8803301237964236, 'number': 1077} | 0.8285 | 0.8664 | 0.8470 | 0.7998 |
| 0.0432 | 21.05 | 400 | 1.6192 | {'precision': 0.8022099447513812, 'recall': 0.8886168910648715, 'f1': 0.843205574912892, 'number': 817} | {'precision': 0.5585585585585585, 'recall': 0.5210084033613446, 'f1': 0.5391304347826087, 'number': 119} | {'precision': 0.9002932551319648, 'recall': 0.8551532033426184, 'f1': 0.8771428571428571, 'number': 1077} | 0.8382 | 0.8490 | 0.8435 | 0.7705 |
| 0.0145 | 31.58 | 600 | 1.4845 | {'precision': 0.8556701030927835, 'recall': 0.9143206854345165, 'f1': 0.8840236686390532, 'number': 817} | {'precision': 0.6274509803921569, 'recall': 0.5378151260504201, 'f1': 0.579185520361991, 'number': 119} | {'precision': 0.8772241992882562, 'recall': 0.9155060352831941, 'f1': 0.8959563834620626, 'number': 1077} | 0.8561 | 0.8927 | 0.8740 | 0.7985 |
| 0.0063 | 42.11 | 800 | 1.4909 | {'precision': 0.8667452830188679, 'recall': 0.8996328029375765, 'f1': 0.8828828828828829, 'number': 817} | {'precision': 0.5766423357664233, 'recall': 0.6638655462184874, 'f1': 0.6171874999999999, 'number': 119} | {'precision': 0.891444342226311, 'recall': 0.8997214484679665, 'f1': 0.8955637707948244, 'number': 1077} | 0.8605 | 0.8857 | 0.8729 | 0.8117 |
| 0.0041 | 52.63 | 1000 | 1.6197 | {'precision': 0.834056399132321, 'recall': 0.9412484700122399, 'f1': 0.8844163312248418, 'number': 817} | {'precision': 0.5945945945945946, 'recall': 0.5546218487394958, 'f1': 0.5739130434782609, 'number': 119} | {'precision': 0.8972407231208372, 'recall': 0.8755803156917363, 'f1': 0.8862781954887218, 'number': 1077} | 0.8532 | 0.8833 | 0.8680 | 0.7912 |
| 0.0043 | 63.16 | 1200 | 1.6797 | {'precision': 0.8763005780346821, 'recall': 0.9277845777233782, 'f1': 0.901307966706302, 'number': 817} | {'precision': 0.6134453781512605, 'recall': 0.6134453781512605, 'f1': 0.6134453781512605, 'number': 119} | {'precision': 0.8998144712430427, 'recall': 0.9006499535747446, 'f1': 0.9002320185614848, 'number': 1077} | 0.8734 | 0.8947 | 0.8839 | 0.8046 |
| 0.0015 | 73.68 | 1400 | 1.5586 | {'precision': 0.8683314415437003, 'recall': 0.9363525091799265, 'f1': 0.9010600706713782, 'number': 817} | {'precision': 0.6486486486486487, 'recall': 0.6050420168067226, 'f1': 0.6260869565217391, 'number': 119} | {'precision': 0.9055555555555556, 'recall': 0.9080779944289693, 'f1': 0.9068150208623087, 'number': 1077} | 0.8760 | 0.9016 | 0.8886 | 0.8093 |
| 0.001 | 84.21 | 1600 | 1.5060 | {'precision': 0.8894230769230769, 'recall': 0.9057527539779682, 'f1': 0.8975136446331109, 'number': 817} | {'precision': 0.711340206185567, 'recall': 0.5798319327731093, 'f1': 0.638888888888889, 'number': 119} | {'precision': 0.8927927927927928, 'recall': 0.9201485608170845, 'f1': 0.9062642889803384, 'number': 1077} | 0.8828 | 0.8942 | 0.8885 | 0.8202 |
| 0.0004 | 94.74 | 1800 | 1.6122 | {'precision': 0.8905882352941177, 'recall': 0.9265605875152999, 'f1': 0.9082183563287344, 'number': 817} | {'precision': 0.6379310344827587, 'recall': 0.6218487394957983, 'f1': 0.6297872340425532, 'number': 119} | {'precision': 0.8964252978918423, 'recall': 0.9080779944289693, 'f1': 0.9022140221402215, 'number': 1077} | 0.8794 | 0.8987 | 0.8889 | 0.8020 |
| 0.0004 | 105.26 | 2000 | 1.6057 | {'precision': 0.8791079812206573, 'recall': 0.9167686658506732, 'f1': 0.8975434391851408, 'number': 817} | {'precision': 0.6260869565217392, 'recall': 0.6050420168067226, 'f1': 0.6153846153846154, 'number': 119} | {'precision': 0.8921124206708976, 'recall': 0.9136490250696379, 'f1': 0.9027522935779816, 'number': 1077} | 0.8720 | 0.8967 | 0.8842 | 0.8057 |
| 0.0004 | 115.79 | 2200 | 1.6179 | {'precision': 0.8848413631022327, 'recall': 0.9216646266829865, 'f1': 0.9028776978417267, 'number': 817} | {'precision': 0.6206896551724138, 'recall': 0.6050420168067226, 'f1': 0.6127659574468085, 'number': 119} | {'precision': 0.8921124206708976, 'recall': 0.9136490250696379, 'f1': 0.9027522935779816, 'number': 1077} | 0.8739 | 0.8987 | 0.8861 | 0.8070 |
| 0.0002 | 126.32 | 2400 | 1.6142 | {'precision': 0.8826291079812206, 'recall': 0.9204406364749081, 'f1': 0.9011384062312762, 'number': 817} | {'precision': 0.6068376068376068, 'recall': 0.5966386554621849, 'f1': 0.6016949152542374, 'number': 119} | {'precision': 0.8965201465201466, 'recall': 0.9090064995357474, 'f1': 0.9027201475334256, 'number': 1077} | 0.8743 | 0.8952 | 0.8846 | 0.8074 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|