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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: deep-haiku-gpt-2 |
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results: [] |
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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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# deep-haiku-gpt-2 |
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This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the [haiku](https://huggingface.co/datasets/statworx/haiku) dataset. |
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## Model description |
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The model is a fine-tuned version of GPT-2 for generation of [Haikus](https://en.wikipedia.org/wiki/Haiku). The model, data and training procedure is inspired by a [blog post by Robert A. Gonsalves](https://towardsdatascience.com/deep-haiku-teaching-gpt-j-to-compose-with-syllable-patterns-5234bca9701). Instead of using a 8bit version of GPT-J 6B, we instead used vanilla GPT-2. From what we saw, the model performance comparable but is much easier to fine-tune. |
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We used the same multitask training approach as in der post, but significantly extended the dataset (almost double the size of the original on). A prepared version of the dataset can be found [here](https://huggingface.co/datasets/statworx/haiku). |
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## Intended uses & limitations |
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The model is intended to generate Haikus. To do so, it was trained using a multitask learning approach (see [Caruana 1997](http://www.cs.cornell.edu/~caruana/mlj97.pdf)) with the following four different tasks: : |
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- topic2graphemes `(keywords = text)` |
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- topic2phonemes `<keyword_phonemes = text_phonemes>` |
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- graphemes2phonemes `[text = text_phonemes]` |
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- phonemes2graphemes `{text_phonemes = text}` |
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To use the model, use an appropriate prompt like `"(dog rain ="` and let the model generate a Haiku given the keyword. |
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## Training and evaluation data |
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We used a collection of existing haikus for training. Furthermore, all haikus were used in their graphemes version as well as a phonemes version. In addition, we extracted key word for all haikus using [KeyBERT](https://github.com/MaartenGr/KeyBERT) and sorted out haikus with a low text quality according to the [GRUEN score](https://github.com/WanzhengZhu/GRUEN). |
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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: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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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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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 10 |
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### Training results |
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### Framework versions |
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- Transformers 4.19.2 |
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- Pytorch 1.11.0+cu102 |
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- Datasets 2.2.1 |
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- Tokenizers 0.12.1 |
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