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---
tags:
- generated_from_trainer
datasets:
- data_registros_layoutv3
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-registros_100
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: data_registros_layoutv3
type: data_registros_layoutv3
config: default
split: test
args: default
metrics:
- name: Precision
type: precision
value: 0.9967585089141004
- name: Recall
type: recall
value: 0.9951456310679612
- name: F1
type: f1
value: 0.9959514170040485
- name: Accuracy
type: accuracy
value: 0.999531542785759
---
<!-- 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-registros_100
This model was trained from scratch on the data_registros_layoutv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0050
- Precision: 0.9968
- Recall: 0.9951
- F1: 0.9960
- Accuracy: 0.9995
## 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: 600
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 4.35 | 100 | 0.0106 | 0.9871 | 0.9935 | 0.9903 | 0.9991 |
| No log | 8.7 | 200 | 0.0073 | 0.9984 | 0.9968 | 0.9976 | 0.9997 |
| No log | 13.04 | 300 | 0.0061 | 0.9968 | 0.9968 | 0.9968 | 0.9997 |
| No log | 17.39 | 400 | 0.0048 | 0.9968 | 0.9984 | 0.9976 | 0.9997 |
| 0.0109 | 21.74 | 500 | 0.0053 | 0.9968 | 0.9968 | 0.9968 | 0.9997 |
| 0.0109 | 26.09 | 600 | 0.0050 | 0.9968 | 0.9951 | 0.9960 | 0.9995 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|