|
|
--- |
|
|
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: 1.1493 |
|
|
- Answer: {'precision': 0.22598870056497175, 'recall': 0.19777503090234858, 'f1': 0.2109426499670402, 'number': 809} |
|
|
- Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} |
|
|
- Question: {'precision': 0.5350523771152297, 'recall': 0.6234741784037559, 'f1': 0.5758889852558543, 'number': 1065} |
|
|
- Overall Precision: 0.4228 |
|
|
- Overall Recall: 0.4134 |
|
|
- Overall F1: 0.4181 |
|
|
- Overall Accuracy: 0.6373 |
|
|
|
|
|
## 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: 3 |
|
|
|
|
|
### Training results |
|
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
|
|
|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
|
|
| 1.6256 | 1.0 | 10 | 1.4524 | {'precision': 0.05670665212649945, 'recall': 0.06427688504326329, 'f1': 0.060254924681344156, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.3111913357400722, 'recall': 0.40469483568075115, 'f1': 0.35183673469387755, 'number': 1065} | 0.2098 | 0.2423 | 0.2249 | 0.4826 | |
|
|
| 1.3478 | 2.0 | 20 | 1.2324 | {'precision': 0.14285714285714285, 'recall': 0.1211372064276885, 'f1': 0.1311036789297659, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4789156626506024, 'recall': 0.5971830985915493, 'f1': 0.5315503552026745, 'number': 1065} | 0.3644 | 0.3683 | 0.3664 | 0.5977 | |
|
|
| 1.155 | 3.0 | 30 | 1.1493 | {'precision': 0.22598870056497175, 'recall': 0.19777503090234858, 'f1': 0.2109426499670402, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.5350523771152297, 'recall': 0.6234741784037559, 'f1': 0.5758889852558543, 'number': 1065} | 0.4228 | 0.4134 | 0.4181 | 0.6373 | |
|
|
|
|
|
|
|
|
### Framework versions |
|
|
|
|
|
- Transformers 4.28.0 |
|
|
- Pytorch 2.0.1+cpu |
|
|
- Datasets 2.14.4 |
|
|
- Tokenizers 0.13.3 |
|
|
|
