gpt2_finetuned_recipe

This model is a fine-tuned version of gpt2 on an nlg dataset from https://github.com/Glorf/recipenlg/tree/main It achieves the following results on the evaluation set:

• Loss: 1.9634

Model description

The model is trained on the jupyter notebook using 10,000 recipes extracted from nlg dataset.

Intended uses & limitations

The use is for personal and educational purposes.

Training and evaluation data

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• 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: 2

Training results

Training Loss	Epoch	Step	Validation Loss
2.1534	1.0	2530	2.0349
1.9073	2.0	5060	1.9634

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", }