|
--- |
|
license: cc-by-nc-sa-4.0 |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- cord-layoutlmv3 |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: layoutlmv3-finetuned-cord_200 |
|
results: |
|
- task: |
|
name: Token Classification |
|
type: token-classification |
|
dataset: |
|
name: cord-layoutlmv3 |
|
type: cord-layoutlmv3 |
|
config: cord |
|
split: train |
|
args: cord |
|
metrics: |
|
- name: Precision |
|
type: precision |
|
value: 0.9033923303834809 |
|
- name: Recall |
|
type: recall |
|
value: 0.9169161676646707 |
|
- name: F1 |
|
type: f1 |
|
value: 0.9101040118870729 |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.9121392190152802 |
|
--- |
|
|
|
<!-- 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_200 |
|
|
|
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cord-layoutlmv3 dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.4529 |
|
- Precision: 0.9034 |
|
- Recall: 0.9169 |
|
- F1: 0.9101 |
|
- Accuracy: 0.9121 |
|
|
|
## 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: 1e-05 |
|
- train_batch_size: 5 |
|
- eval_batch_size: 5 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- training_steps: 3000 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| No log | 6.25 | 250 | 1.0785 | 0.6815 | 0.7575 | 0.7175 | 0.7780 | |
|
| 1.3902 | 12.5 | 500 | 0.5871 | 0.8542 | 0.8683 | 0.8612 | 0.8604 | |
|
| 1.3902 | 18.75 | 750 | 0.4572 | 0.8728 | 0.8937 | 0.8831 | 0.8905 | |
|
| 0.298 | 25.0 | 1000 | 0.3947 | 0.8936 | 0.9117 | 0.9026 | 0.9092 | |
|
| 0.298 | 31.25 | 1250 | 0.3925 | 0.8982 | 0.9177 | 0.9078 | 0.9117 | |
|
| 0.1023 | 37.5 | 1500 | 0.4290 | 0.8908 | 0.9102 | 0.9004 | 0.9041 | |
|
| 0.1023 | 43.75 | 1750 | 0.4220 | 0.8980 | 0.9162 | 0.9070 | 0.9117 | |
|
| 0.0475 | 50.0 | 2000 | 0.4755 | 0.8944 | 0.9064 | 0.9004 | 0.8990 | |
|
| 0.0475 | 56.25 | 2250 | 0.4635 | 0.8992 | 0.9147 | 0.9069 | 0.9070 | |
|
| 0.0296 | 62.5 | 2500 | 0.4475 | 0.9019 | 0.9154 | 0.9086 | 0.9117 | |
|
| 0.0296 | 68.75 | 2750 | 0.4484 | 0.9004 | 0.9139 | 0.9071 | 0.9079 | |
|
| 0.0242 | 75.0 | 3000 | 0.4529 | 0.9034 | 0.9169 | 0.9101 | 0.9121 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.21.2 |
|
- Pytorch 1.12.1+cu113 |
|
- Datasets 2.4.0 |
|
- Tokenizers 0.12.1 |
|
|
