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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: test
args: cord
metrics:
- name: Precision
type: precision
value: 0.9135893648449039
- name: Recall
type: recall
value: 0.9258982035928144
- name: F1
type: f1
value: 0.9197026022304833
- name: Accuracy
type: accuracy
value: 0.9252971137521222
---
<!-- 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: 0.3248
- Precision: 0.9136
- Recall: 0.9259
- F1: 0.9197
- Accuracy: 0.9253
## 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 | 4.17 | 250 | 1.0188 | 0.7447 | 0.7949 | 0.7690 | 0.8031 |
| 1.4061 | 8.33 | 500 | 0.5545 | 0.8420 | 0.8653 | 0.8535 | 0.8616 |
| 1.4061 | 12.5 | 750 | 0.4298 | 0.8884 | 0.9057 | 0.8970 | 0.9045 |
| 0.3563 | 16.67 | 1000 | 0.3477 | 0.9094 | 0.9244 | 0.9169 | 0.9295 |
| 0.3563 | 20.83 | 1250 | 0.3189 | 0.9137 | 0.9274 | 0.9205 | 0.9312 |
| 0.1617 | 25.0 | 1500 | 0.3189 | 0.9210 | 0.9341 | 0.9275 | 0.9393 |
| 0.1617 | 29.17 | 1750 | 0.3158 | 0.9096 | 0.9259 | 0.9177 | 0.9300 |
| 0.0942 | 33.33 | 2000 | 0.3198 | 0.9117 | 0.9274 | 0.9195 | 0.9283 |
| 0.0942 | 37.5 | 2250 | 0.3259 | 0.9112 | 0.9289 | 0.9199 | 0.9300 |
| 0.0674 | 41.67 | 2500 | 0.3248 | 0.9136 | 0.9259 | 0.9197 | 0.9253 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
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