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---
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: train
args: cord
metrics:
- name: Precision
type: precision
value: 0.8719646799116998
- name: Recall
type: recall
value: 0.8869760479041916
- name: F1
type: f1
value: 0.8794063079777364
- name: Accuracy
type: accuracy
value: 0.8790322580645161
---
<!-- 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_100
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: 0.7215
- Precision: 0.8720
- Recall: 0.8870
- F1: 0.8794
- Accuracy: 0.8790
## 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: 2500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 12.5 | 250 | 1.0892 | 0.7345 | 0.7867 | 0.7597 | 0.7806 |
| 1.3039 | 25.0 | 500 | 0.7150 | 0.8054 | 0.8428 | 0.8237 | 0.8281 |
| 1.3039 | 37.5 | 750 | 0.6320 | 0.8335 | 0.8615 | 0.8473 | 0.8540 |
| 0.2171 | 50.0 | 1000 | 0.6427 | 0.8651 | 0.8832 | 0.8741 | 0.8722 |
| 0.2171 | 62.5 | 1250 | 0.6640 | 0.8672 | 0.8847 | 0.8759 | 0.8765 |
| 0.0654 | 75.0 | 1500 | 0.6758 | 0.8650 | 0.8825 | 0.8737 | 0.8731 |
| 0.0654 | 87.5 | 1750 | 0.7028 | 0.8684 | 0.8840 | 0.8761 | 0.8765 |
| 0.0338 | 100.0 | 2000 | 0.7252 | 0.8710 | 0.8847 | 0.8778 | 0.8769 |
| 0.0338 | 112.5 | 2250 | 0.7227 | 0.8710 | 0.8847 | 0.8778 | 0.8778 |
| 0.0257 | 125.0 | 2500 | 0.7215 | 0.8720 | 0.8870 | 0.8794 | 0.8790 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
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