metadata
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_100
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: cord-layoutlmv3
type: cord-layoutlmv3
config: cord
split: test
args: cord
metrics:
- name: Precision
type: precision
value: 0.9479940564635958
- name: Recall
type: recall
value: 0.9550898203592815
- name: F1
type: f1
value: 0.9515287099179717
- name: Accuracy
type: accuracy
value: 0.9562818336162988
layoutlmv3-finetuned-cord_100
This model is a fine-tuned version of microsoft/layoutlmv3-base on the cord-layoutlmv3 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2163
- Precision: 0.9480
- Recall: 0.9551
- F1: 0.9515
- Accuracy: 0.9563
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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2500
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
No log | 3.12 | 250 | 0.8807 | 0.7910 | 0.8301 | 0.8101 | 0.8247 |
1.2597 | 6.25 | 500 | 0.4375 | 0.8808 | 0.9072 | 0.8938 | 0.9117 |
1.2597 | 9.38 | 750 | 0.3119 | 0.9185 | 0.9364 | 0.9274 | 0.9423 |
0.2869 | 12.5 | 1000 | 0.2700 | 0.9340 | 0.9424 | 0.9382 | 0.9452 |
0.2869 | 15.62 | 1250 | 0.2401 | 0.9429 | 0.9513 | 0.9471 | 0.9559 |
0.1378 | 18.75 | 1500 | 0.2295 | 0.9415 | 0.9521 | 0.9468 | 0.9550 |
0.1378 | 21.88 | 1750 | 0.2183 | 0.9523 | 0.9566 | 0.9544 | 0.9580 |
0.0866 | 25.0 | 2000 | 0.2190 | 0.9523 | 0.9566 | 0.9544 | 0.9576 |
0.0866 | 28.12 | 2250 | 0.2168 | 0.9508 | 0.9551 | 0.9529 | 0.9567 |
0.066 | 31.25 | 2500 | 0.2163 | 0.9480 | 0.9551 | 0.9515 | 0.9563 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.0
- Datasets 2.10.1
- Tokenizers 0.13.2