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license: mit |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: 22_12_13_luther_blocks_larger_fp16_20ep |
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results: [] |
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language: |
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- de |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# 22_12_13_luther_blocks_larger_fp16_20ep |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.5847 |
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- Accuracy: 0.3168 |
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## Model description |
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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) |
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## Intended uses & limitations |
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This is to test the capabilities of the GPT-2 transformer architecture. |
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## Training and evaluation data |
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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. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 16 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 20.0 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| No log | 1.6 | 50 | 4.6218 | 0.2156 | |
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| 8.1175 | 3.22 | 100 | 4.0404 | 0.2633 | |
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| 8.1175 | 4.83 | 150 | 3.8120 | 0.2871 | |
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| 3.734 | 6.44 | 200 | 3.7062 | 0.2997 | |
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| 3.734 | 8.06 | 250 | 3.6382 | 0.3082 | |
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| 3.3639 | 9.67 | 300 | 3.6108 | 0.3128 | |
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| 3.3639 | 11.29 | 350 | 3.6012 | 0.3148 | |
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| 3.1363 | 12.89 | 400 | 3.5847 | 0.3168 | |
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| 3.1363 | 14.51 | 450 | 3.5914 | 0.3180 | |
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| 2.9884 | 16.13 | 500 | 3.5954 | 0.3177 | |
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| 2.9884 | 17.73 | 550 | 3.6001 | 0.3176 | |
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| 2.8748 | 19.35 | 600 | 3.6048 | 0.3188 | |
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### Framework versions |
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- Transformers 4.26.0.dev0 |
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- Pytorch 1.13.0 |
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- Datasets 2.7.1 |
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- Tokenizers 0.12.1 |
