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---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- data_cedulas_layoutv3
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-cedulas_v3
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: data_cedulas_layoutv3
type: data_cedulas_layoutv3
config: default
split: test
args: default
metrics:
- name: Precision
type: precision
value: 0.8991596638655462
- name: Recall
type: recall
value: 0.9067796610169492
- name: F1
type: f1
value: 0.9029535864978903
- name: Accuracy
type: accuracy
value: 0.9816565809379728
---
<!-- 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-cedulas_v3
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the data_cedulas_layoutv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0832
- Precision: 0.8992
- Recall: 0.9068
- F1: 0.9030
- Accuracy: 0.9817
## 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: 2500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 3.12 | 250 | 0.7409 | 0.2850 | 0.2729 | 0.2788 | 0.8614 |
| 0.9048 | 6.25 | 500 | 0.3660 | 0.6222 | 0.6559 | 0.6386 | 0.9393 |
| 0.9048 | 9.38 | 750 | 0.2132 | 0.7492 | 0.7593 | 0.7542 | 0.9544 |
| 0.2923 | 12.5 | 1000 | 0.1467 | 0.7830 | 0.7949 | 0.7889 | 0.9661 |
| 0.2923 | 15.62 | 1250 | 0.1172 | 0.8114 | 0.8237 | 0.8175 | 0.9701 |
| 0.1445 | 18.75 | 1500 | 0.1013 | 0.8560 | 0.8763 | 0.8660 | 0.9766 |
| 0.1445 | 21.88 | 1750 | 0.0952 | 0.8811 | 0.8915 | 0.8863 | 0.9794 |
| 0.0956 | 25.0 | 2000 | 0.0876 | 0.8923 | 0.8983 | 0.8953 | 0.9807 |
| 0.0956 | 28.12 | 2250 | 0.0840 | 0.9005 | 0.9051 | 0.9028 | 0.9811 |
| 0.0766 | 31.25 | 2500 | 0.0832 | 0.8992 | 0.9068 | 0.9030 | 0.9817 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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