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---
license: mit
base_model: microsoft/layoutlm-base-uncased
tags:
- generated_from_trainer
datasets:
- funsd
model-index:
- name: layoutlm-funsd1
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-funsd1
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: 0.6746
- Answer: {'precision': 0.6505771248688352, 'recall': 0.7663782447466008, 'f1': 0.7037457434733257, 'number': 809}
- Header: {'precision': 0.20930232558139536, 'recall': 0.15126050420168066, 'f1': 0.17560975609756097, 'number': 119}
- Question: {'precision': 0.7188284518828452, 'recall': 0.8065727699530516, 'f1': 0.7601769911504423, 'number': 1065}
- Overall Precision: 0.6701
- Overall Recall: 0.7511
- Overall F1: 0.7083
- Overall Accuracy: 0.7973
## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.8511 | 1.0 | 10 | 1.6077 | {'precision': 0.01362088535754824, 'recall': 0.014833127317676144, 'f1': 0.014201183431952664, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.17871759890859482, 'recall': 0.12300469483568074, 'f1': 0.1457174638487208, 'number': 1065} | 0.0886 | 0.0718 | 0.0793 | 0.3669 |
| 1.4863 | 2.0 | 20 | 1.2821 | {'precision': 0.14936708860759493, 'recall': 0.14585908529048208, 'f1': 0.14759224515322075, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4211309523809524, 'recall': 0.5314553990610329, 'f1': 0.46990452469904526, 'number': 1065} | 0.3204 | 0.3432 | 0.3314 | 0.5815 |
| 1.1566 | 3.0 | 30 | 1.0398 | {'precision': 0.38341968911917096, 'recall': 0.3658838071693449, 'f1': 0.3744465528146742, 'number': 809} | {'precision': 0.04, 'recall': 0.008403361344537815, 'f1': 0.01388888888888889, 'number': 119} | {'precision': 0.5764705882352941, 'recall': 0.644131455399061, 'f1': 0.6084257206208424, 'number': 1065} | 0.4947 | 0.4932 | 0.4940 | 0.6493 |
| 0.9277 | 4.0 | 40 | 0.8788 | {'precision': 0.5094339622641509, 'recall': 0.6007416563658838, 'f1': 0.5513329551900171, 'number': 809} | {'precision': 0.19047619047619047, 'recall': 0.06722689075630252, 'f1': 0.09937888198757765, 'number': 119} | {'precision': 0.6472172351885098, 'recall': 0.6769953051643193, 'f1': 0.6617714547957778, 'number': 1065} | 0.5758 | 0.6096 | 0.5922 | 0.7266 |
| 0.7448 | 5.0 | 50 | 0.7982 | {'precision': 0.5696594427244582, 'recall': 0.6823238566131026, 'f1': 0.6209223847019122, 'number': 809} | {'precision': 0.2, 'recall': 0.11764705882352941, 'f1': 0.14814814814814817, 'number': 119} | {'precision': 0.6689478186484175, 'recall': 0.7342723004694836, 'f1': 0.7000895255147717, 'number': 1065} | 0.6105 | 0.6764 | 0.6418 | 0.7475 |
| 0.6273 | 6.0 | 60 | 0.7378 | {'precision': 0.6345549738219896, 'recall': 0.7490729295426453, 'f1': 0.6870748299319728, 'number': 809} | {'precision': 0.21052631578947367, 'recall': 0.13445378151260504, 'f1': 0.1641025641025641, 'number': 119} | {'precision': 0.6871270247229326, 'recall': 0.7568075117370892, 'f1': 0.7202859696157283, 'number': 1065} | 0.6479 | 0.7165 | 0.6805 | 0.7778 |
| 0.5778 | 7.0 | 70 | 0.6971 | {'precision': 0.6439075630252101, 'recall': 0.757725587144623, 'f1': 0.6961953435547985, 'number': 809} | {'precision': 0.20238095238095238, 'recall': 0.14285714285714285, 'f1': 0.16748768472906403, 'number': 119} | {'precision': 0.6765412329863891, 'recall': 0.7934272300469484, 'f1': 0.7303370786516853, 'number': 1065} | 0.6455 | 0.7401 | 0.6896 | 0.7825 |
| 0.5262 | 8.0 | 80 | 0.6989 | {'precision': 0.6372141372141372, 'recall': 0.757725587144623, 'f1': 0.6922642574816488, 'number': 809} | {'precision': 0.20689655172413793, 'recall': 0.15126050420168066, 'f1': 0.17475728155339806, 'number': 119} | {'precision': 0.7364685004436557, 'recall': 0.7793427230046949, 'f1': 0.7572992700729927, 'number': 1065} | 0.6714 | 0.7331 | 0.7009 | 0.7963 |
| 0.4867 | 9.0 | 90 | 0.6756 | {'precision': 0.6428571428571429, 'recall': 0.7564894932014833, 'f1': 0.6950596252129472, 'number': 809} | {'precision': 0.1935483870967742, 'recall': 0.15126050420168066, 'f1': 0.169811320754717, 'number': 119} | {'precision': 0.7079207920792079, 'recall': 0.8056338028169014, 'f1': 0.7536231884057971, 'number': 1065} | 0.6593 | 0.7466 | 0.7002 | 0.7951 |
| 0.4757 | 10.0 | 100 | 0.6746 | {'precision': 0.6505771248688352, 'recall': 0.7663782447466008, 'f1': 0.7037457434733257, 'number': 809} | {'precision': 0.20930232558139536, 'recall': 0.15126050420168066, 'f1': 0.17560975609756097, 'number': 119} | {'precision': 0.7188284518828452, 'recall': 0.8065727699530516, 'f1': 0.7601769911504423, 'number': 1065} | 0.6701 | 0.7511 | 0.7083 | 0.7973 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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