license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: gpt2_finetuned_new_10000recipe_chicken
results: []
gpt2_finetuned_new_10000recipe_chicken
This model is a fine-tuned version of gpt2 using 10,000 chicken recipes with no_duplicated titles extracted from nlg dataset. It achieves the following results on the evaluation set:
- Loss: 1.6760
Model description
This model is a fine-tuned version of gpt2 using 10,000 chicken recipes extracted from nlg dataset.
It achieves the following results on the evaluation set:
- Loss: 1.43510
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 |
|---|---|---|---|
| 1.9414 | 1.0 | 2544 | 1.8198 |
| 1.6154 | 2.0 | 5088 | 1.7056 |
| 1.4351 | 3.0 | 7632 | 1.6760 |
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", }