22_12_13_luther_blocks_larger_fp16_20ep
This model is a fine-tuned version of 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