---
license: mit
tags:
- generated_from_trainer
datasets:
- kantinewinkel
model-index:
- name: kantinewinkel-repo
  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. -->

# kantinewinkel-repo

This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the kantinewinkel dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0083
- Cash: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5}
- Date: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 44}
- Subtotal: {'precision': 0.95, 'recall': 1.0, 'f1': 0.9743589743589743, 'number': 19}
- Total: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}
- Overall Precision: 0.9886
- Overall Recall: 1.0
- Overall F1: 0.9943
- Overall Accuracy: 0.9991

## 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-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Cash                                                      | Date                                                       | Subtotal                                                                   | Total                                                      | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------:|:----------------------------------------------------------:|:--------------------------------------------------------------------------:|:----------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.0476        | 8.7   | 200  | 0.0083          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 44} | {'precision': 0.95, 'recall': 1.0, 'f1': 0.9743589743589743, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | 0.9886            | 1.0            | 0.9943     | 0.9991           |
| 0.0003        | 17.39 | 400  | 0.0092          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 44} | {'precision': 0.95, 'recall': 1.0, 'f1': 0.9743589743589743, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | 0.9886            | 1.0            | 0.9943     | 0.9991           |
| 0.0005        | 26.09 | 600  | 0.0080          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 44} | {'precision': 0.95, 'recall': 1.0, 'f1': 0.9743589743589743, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | 0.9886            | 1.0            | 0.9943     | 0.9991           |
| 0.0           | 34.78 | 800  | 0.0082          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 44} | {'precision': 0.95, 'recall': 1.0, 'f1': 0.9743589743589743, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | 0.9886            | 1.0            | 0.9943     | 0.9991           |
| 0.0001        | 43.48 | 1000 | 0.0081          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 44} | {'precision': 0.95, 'recall': 1.0, 'f1': 0.9743589743589743, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | 0.9886            | 1.0            | 0.9943     | 0.9991           |


### Framework versions

- Transformers 4.29.2
- Pytorch 1.13.1
- Datasets 2.12.0
- Tokenizers 0.13.2