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---
tags:
- generated_from_trainer
model-index:
- name: icdar23-entrydetector_plaintext_breaks_indents_left_ref_right_ref
results: []
---
<!-- 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. -->
# icdar23-entrydetector_plaintext_breaks_indents_left_ref_right_ref
This model is a fine-tuned version of [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0084
- Ebegin: {'precision': 0.9916415914409896, 'recall': 0.9880079946702198, 'f1': 0.9898214583680961, 'number': 3002}
- Eend: {'precision': 0.9879919946631087, 'recall': 0.9873333333333333, 'f1': 0.9876625541847281, 'number': 3000}
- Overall Precision: 0.9898
- Overall Recall: 0.9877
- Overall F1: 0.9887
- Overall Accuracy: 0.9982
## 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: 0.0001
- 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: 6000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.07 | 300 | 0.0364 | 0.9697 | 0.9694 | 0.9695 | 0.9949 |
| 0.1737 | 0.14 | 600 | 0.0146 | 0.9849 | 0.9880 | 0.9865 | 0.9977 |
| 0.1737 | 0.21 | 900 | 0.0092 | 0.9835 | 0.9929 | 0.9881 | 0.9980 |
| 0.0158 | 0.29 | 1200 | 0.0074 | 0.9904 | 0.9912 | 0.9908 | 0.9984 |
| 0.0098 | 0.36 | 1500 | 0.0058 | 0.9866 | 0.9943 | 0.9904 | 0.9984 |
| 0.0098 | 0.43 | 1800 | 0.0075 | 0.9883 | 0.9898 | 0.9890 | 0.9982 |
| 0.0073 | 0.5 | 2100 | 0.0068 | 0.9962 | 0.9815 | 0.9888 | 0.9981 |
| 0.0073 | 0.57 | 2400 | 0.0065 | 0.9899 | 0.9900 | 0.9899 | 0.9983 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
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