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---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: gpt2_finetuned_new_10000recipe_chicken
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. -->
# gpt2_finetuned_new_10000recipe_chicken
This model is a fine-tuned version of [gpt2](https://huggingface.co/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](https://huggingface.co/gpt2) using 10,000 chicken recipes extracted from nlg dataset. <br>
It achieves the following results on the evaluation set:
- Loss: 1.43510
## 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",
} |