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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: 0.9349593495934959
- name: Recall
type: recall
value: 0.9468562874251497
- name: F1
type: f1
value: 0.9408702119747119
- name: Accuracy
type: accuracy
value: 0.9473684210526315
---
<!-- 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: 0.2702
- Precision: 0.9350
- Recall: 0.9469
- F1: 0.9409
- Accuracy: 0.9474
## 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 | 1.0496 | 0.6714 | 0.7507 | 0.7088 | 0.7746 |
| 1.4245 | 8.33 | 500 | 0.5492 | 0.8401 | 0.8728 | 0.8561 | 0.8735 |
| 1.4245 | 12.5 | 750 | 0.3773 | 0.8934 | 0.9162 | 0.9047 | 0.9240 |
| 0.3461 | 16.67 | 1000 | 0.3212 | 0.9287 | 0.9364 | 0.9325 | 0.9380 |
| 0.3461 | 20.83 | 1250 | 0.2888 | 0.9276 | 0.9401 | 0.9338 | 0.9440 |
| 0.1502 | 25.0 | 1500 | 0.2749 | 0.9299 | 0.9431 | 0.9365 | 0.9474 |
| 0.1502 | 29.17 | 1750 | 0.2741 | 0.9321 | 0.9446 | 0.9383 | 0.9469 |
| 0.0866 | 33.33 | 2000 | 0.2715 | 0.9328 | 0.9454 | 0.9390 | 0.9465 |
| 0.0866 | 37.5 | 2250 | 0.2740 | 0.9314 | 0.9446 | 0.9379 | 0.9452 |
| 0.0635 | 41.67 | 2500 | 0.2702 | 0.9350 | 0.9469 | 0.9409 | 0.9474 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
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