File size: 9,305 Bytes
7104f0c 1038f7e 7104f0c |
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 79 80 81 |
---
tags:
- generated_from_trainer
datasets:
- funsd
base_model: microsoft/layoutlm-base-uncased
model-index:
- name: layoutlm-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. -->
# layoutlm-funsd
This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7960
- Answer: {'precision': 0.7169603524229075, 'recall': 0.8046971569839307, 'f1': 0.7582993593476993, 'number': 809}
- Header: {'precision': 0.36619718309859156, 'recall': 0.4369747899159664, 'f1': 0.39846743295019166, 'number': 119}
- Question: {'precision': 0.7883408071748879, 'recall': 0.8253521126760563, 'f1': 0.8064220183486238, 'number': 1065}
- Overall Precision: 0.7307
- Overall Recall: 0.7938
- Overall F1: 0.7609
- Overall Accuracy: 0.8081
## 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: 3e-05
- train_batch_size: 6
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.6386 | 1.0 | 25 | 1.2949 | {'precision': 0.08352668213457076, 'recall': 0.08899876390605686, 'f1': 0.08617594254937162, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.36874571624400276, 'recall': 0.5051643192488263, 'f1': 0.42630744849445323, 'number': 1065} | 0.2628 | 0.3061 | 0.2828 | 0.5116 |
| 1.0433 | 2.0 | 50 | 0.8005 | {'precision': 0.5965447154471545, 'recall': 0.7255871446229913, 'f1': 0.6547685443390964, 'number': 809} | {'precision': 0.1111111111111111, 'recall': 0.058823529411764705, 'f1': 0.07692307692307691, 'number': 119} | {'precision': 0.6574487065120428, 'recall': 0.692018779342723, 'f1': 0.6742909423604757, 'number': 1065} | 0.6139 | 0.6678 | 0.6398 | 0.7293 |
| 0.6891 | 3.0 | 75 | 0.6695 | {'precision': 0.6335650446871897, 'recall': 0.788627935723115, 'f1': 0.7026431718061674, 'number': 809} | {'precision': 0.3246753246753247, 'recall': 0.21008403361344538, 'f1': 0.25510204081632654, 'number': 119} | {'precision': 0.7085862966175195, 'recall': 0.7671361502347418, 'f1': 0.7366997294860236, 'number': 1065} | 0.6616 | 0.7426 | 0.6998 | 0.7752 |
| 0.532 | 4.0 | 100 | 0.6270 | {'precision': 0.6573787409700722, 'recall': 0.7873918417799752, 'f1': 0.7165354330708661, 'number': 809} | {'precision': 0.2361111111111111, 'recall': 0.2857142857142857, 'f1': 0.25855513307984795, 'number': 119} | {'precision': 0.7153284671532847, 'recall': 0.828169014084507, 'f1': 0.7676240208877285, 'number': 1065} | 0.6620 | 0.7792 | 0.7158 | 0.7961 |
| 0.4184 | 5.0 | 125 | 0.6174 | {'precision': 0.6837160751565762, 'recall': 0.8096415327564895, 'f1': 0.7413695529145445, 'number': 809} | {'precision': 0.3063063063063063, 'recall': 0.2857142857142857, 'f1': 0.2956521739130435, 'number': 119} | {'precision': 0.7734657039711191, 'recall': 0.8046948356807512, 'f1': 0.7887712839392544, 'number': 1065} | 0.7102 | 0.7757 | 0.7415 | 0.8025 |
| 0.3264 | 6.0 | 150 | 0.6493 | {'precision': 0.6905537459283387, 'recall': 0.7861557478368356, 'f1': 0.7352601156069365, 'number': 809} | {'precision': 0.310126582278481, 'recall': 0.4117647058823529, 'f1': 0.35379061371841153, 'number': 119} | {'precision': 0.7713523131672598, 'recall': 0.8140845070422535, 'f1': 0.7921425308359983, 'number': 1065} | 0.7045 | 0.7787 | 0.7398 | 0.8008 |
| 0.2661 | 7.0 | 175 | 0.6587 | {'precision': 0.6857440166493236, 'recall': 0.8145859085290482, 'f1': 0.7446327683615819, 'number': 809} | {'precision': 0.32575757575757575, 'recall': 0.36134453781512604, 'f1': 0.3426294820717131, 'number': 119} | {'precision': 0.7720970537261699, 'recall': 0.8366197183098592, 'f1': 0.8030644434429923, 'number': 1065} | 0.7089 | 0.7993 | 0.7514 | 0.8038 |
| 0.2246 | 8.0 | 200 | 0.7115 | {'precision': 0.7111356119073869, 'recall': 0.7972805933250927, 'f1': 0.7517482517482517, 'number': 809} | {'precision': 0.2983425414364641, 'recall': 0.453781512605042, 'f1': 0.36, 'number': 119} | {'precision': 0.7891402714932126, 'recall': 0.8187793427230047, 'f1': 0.8036866359447005, 'number': 1065} | 0.7164 | 0.7883 | 0.7506 | 0.8074 |
| 0.1928 | 9.0 | 225 | 0.7130 | {'precision': 0.7094668117519043, 'recall': 0.8059332509270705, 'f1': 0.7546296296296295, 'number': 809} | {'precision': 0.3178294573643411, 'recall': 0.3445378151260504, 'f1': 0.33064516129032256, 'number': 119} | {'precision': 0.7908025247971145, 'recall': 0.8234741784037559, 'f1': 0.8068077276908925, 'number': 1065} | 0.7279 | 0.7878 | 0.7566 | 0.8042 |
| 0.1598 | 10.0 | 250 | 0.7375 | {'precision': 0.7242937853107345, 'recall': 0.792336217552534, 'f1': 0.756788665879575, 'number': 809} | {'precision': 0.375, 'recall': 0.42857142857142855, 'f1': 0.39999999999999997, 'number': 119} | {'precision': 0.788858939802336, 'recall': 0.8244131455399061, 'f1': 0.8062442607897153, 'number': 1065} | 0.7357 | 0.7878 | 0.7608 | 0.8099 |
| 0.1444 | 11.0 | 275 | 0.7719 | {'precision': 0.7027896995708155, 'recall': 0.8096415327564895, 'f1': 0.7524411257897761, 'number': 809} | {'precision': 0.34814814814814815, 'recall': 0.3949579831932773, 'f1': 0.3700787401574803, 'number': 119} | {'precision': 0.7825311942959001, 'recall': 0.8244131455399061, 'f1': 0.8029263831732967, 'number': 1065} | 0.7218 | 0.7928 | 0.7556 | 0.8008 |
| 0.1251 | 12.0 | 300 | 0.7758 | {'precision': 0.7133479212253829, 'recall': 0.8059332509270705, 'f1': 0.7568195008705745, 'number': 809} | {'precision': 0.38095238095238093, 'recall': 0.40336134453781514, 'f1': 0.39183673469387753, 'number': 119} | {'precision': 0.7880434782608695, 'recall': 0.8169014084507042, 'f1': 0.8022130013831259, 'number': 1065} | 0.7323 | 0.7878 | 0.7590 | 0.8077 |
| 0.1124 | 13.0 | 325 | 0.7878 | {'precision': 0.7150776053215078, 'recall': 0.7972805933250927, 'f1': 0.7539450613676213, 'number': 809} | {'precision': 0.38848920863309355, 'recall': 0.453781512605042, 'f1': 0.4186046511627907, 'number': 119} | {'precision': 0.7922312556458898, 'recall': 0.8234741784037559, 'f1': 0.8075506445672191, 'number': 1065} | 0.7337 | 0.7908 | 0.7612 | 0.8094 |
| 0.1077 | 14.0 | 350 | 0.7945 | {'precision': 0.7136612021857923, 'recall': 0.8071693448702101, 'f1': 0.7575406032482598, 'number': 809} | {'precision': 0.36619718309859156, 'recall': 0.4369747899159664, 'f1': 0.39846743295019166, 'number': 119} | {'precision': 0.7887197851387645, 'recall': 0.8272300469483568, 'f1': 0.8075160403299725, 'number': 1065} | 0.7295 | 0.7958 | 0.7612 | 0.8098 |
| 0.1001 | 15.0 | 375 | 0.7960 | {'precision': 0.7169603524229075, 'recall': 0.8046971569839307, 'f1': 0.7582993593476993, 'number': 809} | {'precision': 0.36619718309859156, 'recall': 0.4369747899159664, 'f1': 0.39846743295019166, 'number': 119} | {'precision': 0.7883408071748879, 'recall': 0.8253521126760563, 'f1': 0.8064220183486238, 'number': 1065} | 0.7307 | 0.7938 | 0.7609 | 0.8081 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
|