|
--- |
|
library_name: transformers |
|
base_model: layoutlmv3 |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- mp-02/cord |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: layoutlmv3-finetuned-cord |
|
results: |
|
- task: |
|
name: Token Classification |
|
type: token-classification |
|
dataset: |
|
name: mp-02/cord |
|
type: mp-02/cord |
|
metrics: |
|
- name: Precision |
|
type: precision |
|
value: 0.963984674329502 |
|
- name: Recall |
|
type: recall |
|
value: 0.9767080745341615 |
|
- name: F1 |
|
type: f1 |
|
value: 0.9703046664095644 |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.9702886247877759 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# layoutlmv3-finetuned-cord |
|
|
|
This model is a fine-tuned version of [layoutlmv3](https://huggingface.co/layoutlmv3) on the mp-02/cord dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.2044 |
|
- Precision: 0.9640 |
|
- Recall: 0.9767 |
|
- F1: 0.9703 |
|
- Accuracy: 0.9703 |
|
|
|
## 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: 5e-05 |
|
- train_batch_size: 14 |
|
- eval_batch_size: 14 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- training_steps: 1000 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| No log | 1.7241 | 100 | 0.4595 | 0.8310 | 0.8859 | 0.8576 | 0.8786 | |
|
| No log | 3.4483 | 200 | 0.2637 | 0.9284 | 0.9565 | 0.9423 | 0.9469 | |
|
| No log | 5.1724 | 300 | 0.2096 | 0.9513 | 0.9697 | 0.9604 | 0.9626 | |
|
| No log | 6.8966 | 400 | 0.2016 | 0.9512 | 0.9689 | 0.96 | 0.9622 | |
|
| 0.3892 | 8.6207 | 500 | 0.2418 | 0.9453 | 0.9658 | 0.9555 | 0.9593 | |
|
| 0.3892 | 10.3448 | 600 | 0.2149 | 0.9579 | 0.9713 | 0.9645 | 0.9660 | |
|
| 0.3892 | 12.0690 | 700 | 0.2090 | 0.9608 | 0.9713 | 0.9660 | 0.9652 | |
|
| 0.3892 | 13.7931 | 800 | 0.2202 | 0.9580 | 0.9728 | 0.9653 | 0.9673 | |
|
| 0.3892 | 15.5172 | 900 | 0.2217 | 0.9595 | 0.9744 | 0.9669 | 0.9682 | |
|
| 0.0278 | 17.2414 | 1000 | 0.2044 | 0.9640 | 0.9767 | 0.9703 | 0.9703 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.44.2 |
|
- Pytorch 2.4.0+cu118 |
|
- Datasets 2.21.0 |
|
- Tokenizers 0.19.1 |
|
|
