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---
tags:
- generated_from_trainer
model-index:
- name: icdar23-entrydetector_plaintext_breaks_indents_left_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
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: 0.0060
- Ebegin: {'precision': 0.9928166351606805, 'recall': 0.9875893192929672, 'f1': 0.9901960784313725, 'number': 2659}
- Eend: {'precision': 0.9984750285932139, 'recall': 0.9786995515695067, 'f1': 0.9884883940366107, 'number': 2676}
- Overall Precision: 0.9956
- Overall Recall: 0.9831
- Overall F1: 0.9893
- 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: 7500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.07 | 300 | 0.0401 | 0.9408 | 0.9725 | 0.9564 | 0.9936 |
| 0.1602 | 0.14 | 600 | 0.0195 | 0.9896 | 0.9741 | 0.9818 | 0.9972 |
| 0.1602 | 0.21 | 900 | 0.0101 | 0.9949 | 0.9875 | 0.9911 | 0.9985 |
| 0.0205 | 0.29 | 1200 | 0.0117 | 0.9860 | 0.9894 | 0.9877 | 0.9979 |
| 0.0104 | 0.36 | 1500 | 0.0091 | 0.9819 | 0.9948 | 0.9883 | 0.9979 |
| 0.0104 | 0.43 | 1800 | 0.0058 | 0.9886 | 0.9933 | 0.9909 | 0.9984 |
| 0.0081 | 0.5 | 2100 | 0.0067 | 0.9892 | 0.9931 | 0.9911 | 0.9984 |
| 0.0081 | 0.57 | 2400 | 0.0049 | 0.9928 | 0.9939 | 0.9934 | 0.9988 |
| 0.0069 | 0.64 | 2700 | 0.0048 | 0.9895 | 0.9931 | 0.9913 | 0.9985 |
| 0.0066 | 0.72 | 3000 | 0.0061 | 0.9971 | 0.9865 | 0.9918 | 0.9985 |
| 0.0066 | 0.79 | 3300 | 0.0042 | 0.9954 | 0.9927 | 0.9940 | 0.9990 |
| 0.0046 | 0.86 | 3600 | 0.0039 | 0.9958 | 0.9923 | 0.9941 | 0.9990 |
| 0.0046 | 0.93 | 3900 | 0.0058 | 0.9835 | 0.9959 | 0.9896 | 0.9981 |
| 0.0052 | 1.0 | 4200 | 0.0055 | 0.9963 | 0.9892 | 0.9927 | 0.9987 |
| 0.003 | 1.07 | 4500 | 0.0051 | 0.9939 | 0.9929 | 0.9934 | 0.9988 |
| 0.003 | 1.14 | 4800 | 0.0075 | 0.9977 | 0.9871 | 0.9924 | 0.9987 |
| 0.0039 | 1.22 | 5100 | 0.0051 | 0.9952 | 0.9922 | 0.9937 | 0.9989 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
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