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---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- data_cartas_layoutv3
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-letter_100
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: data_cartas_layoutv3
type: data_cartas_layoutv3
config: default
split: test
args: default
metrics:
- name: Precision
type: precision
value: 0.7411894273127754
- name: Recall
type: recall
value: 0.8672680412371134
- name: F1
type: f1
value: 0.7992874109263659
- name: Accuracy
type: accuracy
value: 0.9631952889969916
---
<!-- 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-letter_100
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the data_cartas_layoutv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1884
- Precision: 0.7412
- Recall: 0.8673
- F1: 0.7993
- Accuracy: 0.9632
## 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: 5e-06
- train_batch_size: 4
- eval_batch_size: 4
- 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 | 3.57 | 250 | 0.4503 | 0.2934 | 0.2242 | 0.2542 | 0.8928 |
| 0.5521 | 7.14 | 500 | 0.2833 | 0.4291 | 0.4639 | 0.4458 | 0.9209 |
| 0.5521 | 10.71 | 750 | 0.2116 | 0.5702 | 0.6753 | 0.6183 | 0.9437 |
| 0.173 | 14.29 | 1000 | 0.1786 | 0.6414 | 0.7835 | 0.7053 | 0.9562 |
| 0.173 | 17.86 | 1250 | 0.1772 | 0.6815 | 0.8492 | 0.7562 | 0.9581 |
| 0.077 | 21.43 | 1500 | 0.1737 | 0.7144 | 0.8737 | 0.7861 | 0.9616 |
| 0.077 | 25.0 | 1750 | 0.1768 | 0.7311 | 0.8724 | 0.7955 | 0.9615 |
| 0.0441 | 28.57 | 2000 | 0.1694 | 0.7726 | 0.8273 | 0.7990 | 0.9646 |
| 0.0441 | 32.14 | 2250 | 0.1874 | 0.7400 | 0.8621 | 0.7964 | 0.9620 |
| 0.0293 | 35.71 | 2500 | 0.1862 | 0.7321 | 0.8698 | 0.7951 | 0.9622 |
| 0.0293 | 39.29 | 2750 | 0.1887 | 0.7332 | 0.8711 | 0.7962 | 0.9620 |
| 0.0237 | 42.86 | 3000 | 0.1884 | 0.7412 | 0.8673 | 0.7993 | 0.9632 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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