File size: 4,860 Bytes
40cf4db | 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 | ---
license: mit
base_model: microsoft/layoutlm-base-uncased
tags:
- generated_from_trainer
datasets:
- funsd
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: 0.7180
- Answer: {'precision': 0.596, 'recall': 0.7367119901112484, 'f1': 0.6589275843007186, 'number': 809}
- Header: {'precision': 0.08571428571428572, 'recall': 0.05042016806722689, 'f1': 0.06349206349206349, 'number': 119}
- Question: {'precision': 0.6859706362153344, 'recall': 0.7896713615023474, 'f1': 0.7341772151898733, 'number': 1065}
- Overall Precision: 0.6285
- Overall Recall: 0.7240
- Overall F1: 0.6729
- Overall Accuracy: 0.7704
## 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: 8
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.6774 | 1.0 | 19 | 1.3758 | {'precision': 0.06839378238341969, 'recall': 0.0815822002472188, 'f1': 0.07440811724915444, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.332824427480916, 'recall': 0.40938967136150234, 'f1': 0.367157894736842, 'number': 1065} | 0.2207 | 0.2519 | 0.2352 | 0.4928 |
| 1.169 | 2.0 | 38 | 0.9500 | {'precision': 0.4467425025853154, 'recall': 0.5339925834363412, 'f1': 0.48648648648648646, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.557753164556962, 'recall': 0.6619718309859155, 'f1': 0.605410047230571, 'number': 1065} | 0.5076 | 0.5705 | 0.5372 | 0.6799 |
| 0.8429 | 3.0 | 57 | 0.7922 | {'precision': 0.5751953125, 'recall': 0.7280593325092707, 'f1': 0.6426623022367702, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.6494676494676495, 'recall': 0.7446009389671362, 'f1': 0.6937882764654418, 'number': 1065} | 0.6051 | 0.6934 | 0.6462 | 0.7457 |
| 0.6915 | 4.0 | 76 | 0.7294 | {'precision': 0.5885262116716122, 'recall': 0.7354758961681088, 'f1': 0.6538461538461539, 'number': 809} | {'precision': 0.05172413793103448, 'recall': 0.025210084033613446, 'f1': 0.03389830508474576, 'number': 119} | {'precision': 0.6642628205128205, 'recall': 0.7784037558685446, 'f1': 0.7168179853004755, 'number': 1065} | 0.6159 | 0.7160 | 0.6622 | 0.7651 |
| 0.6221 | 5.0 | 95 | 0.7180 | {'precision': 0.596, 'recall': 0.7367119901112484, 'f1': 0.6589275843007186, 'number': 809} | {'precision': 0.08571428571428572, 'recall': 0.05042016806722689, 'f1': 0.06349206349206349, 'number': 119} | {'precision': 0.6859706362153344, 'recall': 0.7896713615023474, 'f1': 0.7341772151898733, 'number': 1065} | 0.6285 | 0.7240 | 0.6729 | 0.7704 |
### Framework versions
- Transformers 4.43.4
- Pytorch 2.4.0+cpu
- Datasets 2.20.0
- Tokenizers 0.19.1
|