---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: 22_12_13_luther_blocks_larger_fp16_20ep
  results: []
language:
- de
---

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# 22_12_13_luther_blocks_larger_fp16_20ep

This model is a fine-tuned version of [stefan-it/german-gpt2-larger](https://huggingface.co/stefan-it/german-gpt2-larger) on a dataset of texts by Martin Luther.
It achieves the following results on the evaluation set:
- Loss: 3.5847
- Accuracy: 0.3168

## Model description

This is a language model used to generate wishes for a happy new year to the readers of "reformiert" a journal in Switzerland (https://www.reformiert.info)

## Intended uses & limitations

This is to test the capabilities of the GPT-2 transformer architecture.

## Training and evaluation data

Automatic split of an edited and "cleaned" version of parts of Luther's writing. Cleaning refers here to the process of eliminating para-texts like page numbering, footnotes, etc.

## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.6   | 50   | 4.6218          | 0.2156   |
| 8.1175        | 3.22  | 100  | 4.0404          | 0.2633   |
| 8.1175        | 4.83  | 150  | 3.8120          | 0.2871   |
| 3.734         | 6.44  | 200  | 3.7062          | 0.2997   |
| 3.734         | 8.06  | 250  | 3.6382          | 0.3082   |
| 3.3639        | 9.67  | 300  | 3.6108          | 0.3128   |
| 3.3639        | 11.29 | 350  | 3.6012          | 0.3148   |
| 3.1363        | 12.89 | 400  | 3.5847          | 0.3168   |
| 3.1363        | 14.51 | 450  | 3.5914          | 0.3180   |
| 2.9884        | 16.13 | 500  | 3.5954          | 0.3177   |
| 2.9884        | 17.73 | 550  | 3.6001          | 0.3176   |
| 2.8748        | 19.35 | 600  | 3.6048          | 0.3188   |


### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.13.0
- Datasets 2.7.1
- Tokenizers 0.12.1