---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: gpt2_finetuned_10000recipe_chicken
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# gpt2_finetuned_10000recipe_chicken

This model is a fine-tuned version of gpt2 on an nlg dataset from https://github.com/Glorf/recipenlg/tree/main which has been subset into recipes containing chicken.
It achieves the following results on the evaluation set:
- Loss: 1.5802

## Model description

This model is a fine-tuned version of [gpt2](https://huggingface.co./gpt2) using 10,000 chicken recipes extracted from nlg dataset. <br>
It achieves the following results on the evaluation set: 
- Loss: 1.3647

## Intended uses & limitations

The use is for personal and educational purposes.

## Training and evaluation data

The model uses 10043 recipes for its training data and 100 recipes for its evaluation data.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.866         | 1.0   | 2511 | 1.7299          |
| 1.5425        | 2.0   | 5022 | 1.6135          |
| 1.3647        | 3.0   | 7533 | 1.5802          |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cpu
- Datasets 2.14.4
- Tokenizers 0.11.0

### Reference

@inproceedings{bien-etal-2020-recipenlg,
    title = "{R}ecipe{NLG}: A Cooking Recipes Dataset for Semi-Structured Text Generation",
    author = "Bie{\'n}, Micha{\l}  and
      Gilski, Micha{\l}  and
      Maciejewska, Martyna  and
      Taisner, Wojciech  and
      Wisniewski, Dawid  and
      Lawrynowicz, Agnieszka",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.4",
    pages = "22--28",
}