Create README.md

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+model-index:
+- name: deep-haiku-gpt-j-6b-8bit
+  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. -->
+
+# deep-haiku-gpt-j-6b-8bit
+
+This model is a fine-tuned version of [gpt-j-6B-8bit](https://huggingface.co/hivemind/gpt-j-6B-8bit) on the [haiku](https://huggingface.co/datasets/statworx/haiku) dataset.
+
+## Model description
+
+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.
+
+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). 
+
+## Intended uses & limitations
+
+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: :
+
+ - topic2graphemes       `(keywords = text)`
+ - topic2phonemes        `<keyword_phonemes = text_phonemes>`
+ - graphemes2phonemes   `[text = text_phonemes]`
+ - phonemes2graphemes    `{text_phonemes = text}`
+ 
+ To use the model, use an appropriate prompt like `"(dog rain ="` and let the model generate a Haiku given the keyword.
+
+## Training and evaluation data
+
+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).
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 2
+- eval_batch_size: 2
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 100
+- num_epochs: 10
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.19.2
+- Pytorch 1.11.0+cu102
+- Datasets 2.2.1
+- Tokenizers 0.12.1