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---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- dataset
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: sougemi_model
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: dataset
type: dataset
config: discharge
split: test
args: discharge
metrics:
- name: Precision
type: precision
value: 0.845360824742268
- name: Recall
type: recall
value: 0.8913043478260869
- name: F1
type: f1
value: 0.8677248677248677
- name: Accuracy
type: accuracy
value: 0.9533678756476683
---
<!-- 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. -->
# sougemi_model
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the dataset created.
It achieves the following results on the evaluation set:
- Loss: 0.1812
- Precision: 0.8454
- Recall: 0.8913
- F1: 0.8677
- Accuracy: 0.9534
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 33.33 | 100 | 0.7803 | 0.8966 | 0.8478 | 0.8715 | 0.9663 |
| No log | 66.67 | 200 | 0.3016 | 0.8696 | 0.8696 | 0.8696 | 0.9767 |
| No log | 100.0 | 300 | 0.1623 | 0.9130 | 0.9130 | 0.9130 | 0.9819 |
| No log | 133.33 | 400 | 0.1680 | 0.8454 | 0.8913 | 0.8677 | 0.9637 |
| 0.5801 | 166.67 | 500 | 0.1812 | 0.8454 | 0.8913 | 0.8677 | 0.9534 |
| 0.5801 | 200.0 | 600 | 0.1231 | 0.8947 | 0.9239 | 0.9091 | 0.9715 |
| 0.5801 | 233.33 | 700 | 0.1363 | 0.8617 | 0.8804 | 0.8710 | 0.9663 |
| 0.5801 | 266.67 | 800 | 0.1949 | 0.8333 | 0.8696 | 0.8511 | 0.9508 |
| 0.5801 | 300.0 | 900 | 0.1749 | 0.8163 | 0.8696 | 0.8421 | 0.9534 |
| 0.0607 | 333.33 | 1000 | 0.1817 | 0.8163 | 0.8696 | 0.8421 | 0.9534 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
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