|
|
--- |
|
|
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: 1.7205 |
|
|
- Answer: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} |
|
|
- Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} |
|
|
- Question: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1070} |
|
|
- Overall Precision: 0.0 |
|
|
- Overall Recall: 0.0 |
|
|
- Overall F1: 0.0 |
|
|
- Overall Accuracy: 0.2854 |
|
|
|
|
|
## 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: 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 | |
|
|
|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------:|:-----------------------------------------------------------:|:------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
|
|
| 1.8104 | 1.0 | 19 | 1.7227 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1070} | 0.0 | 0.0 | 0.0 | 0.2854 | |
|
|
| 1.7658 | 2.0 | 38 | 1.7254 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1070} | 0.0 | 0.0 | 0.0 | 0.2854 | |
|
|
| 1.7511 | 3.0 | 57 | 1.7137 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1070} | 0.0 | 0.0 | 0.0 | 0.2854 | |
|
|
| 1.7532 | 4.0 | 76 | 1.7184 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1070} | 0.0 | 0.0 | 0.0 | 0.2854 | |
|
|
| 1.7589 | 5.0 | 95 | 1.7141 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1070} | 0.0 | 0.0 | 0.0 | 0.2854 | |
|
|
| 1.748 | 6.0 | 114 | 1.7016 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1070} | 0.0 | 0.0 | 0.0 | 0.2854 | |
|
|
| 1.7487 | 7.0 | 133 | 1.7239 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1070} | 0.0 | 0.0 | 0.0 | 0.2854 | |
|
|
| 1.7483 | 8.0 | 152 | 1.7207 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1070} | 0.0 | 0.0 | 0.0 | 0.2854 | |
|
|
| 1.7465 | 9.0 | 171 | 1.7119 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1070} | 0.0 | 0.0 | 0.0 | 0.2854 | |
|
|
| 1.7458 | 10.0 | 190 | 1.7169 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1070} | 0.0 | 0.0 | 0.0 | 0.2854 | |
|
|
| 1.7419 | 11.0 | 209 | 1.7125 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1070} | 0.0 | 0.0 | 0.0 | 0.2854 | |
|
|
| 1.7425 | 12.0 | 228 | 1.7218 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1070} | 0.0 | 0.0 | 0.0 | 0.2854 | |
|
|
| 1.7424 | 13.0 | 247 | 1.7250 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1070} | 0.0 | 0.0 | 0.0 | 0.2854 | |
|
|
| 1.7412 | 14.0 | 266 | 1.7232 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1070} | 0.0 | 0.0 | 0.0 | 0.2854 | |
|
|
| 1.7389 | 15.0 | 285 | 1.7205 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1070} | 0.0 | 0.0 | 0.0 | 0.2854 | |
|
|
|
|
|
|
|
|
### Framework versions |
|
|
|
|
|
- Transformers 4.28.0 |
|
|
- Pytorch 2.0.1+cu118 |
|
|
- Datasets 2.12.0 |
|
|
- Tokenizers 0.13.3 |
|
|
|
