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license: cc-by-nc-4.0 |
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--- |
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A heavily curated dataset from recipe-nlg (source="**Gathered**" only). A lot of scraping artifacts, typographical errors, unicode, empty and very short recipes were removed. |
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Then it has been formated into Alpaca instruction set with Instructions, Input and Output. The total number of recipes went from ~2M2 (original dataset) to ~500K. |
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Obviously, it's still not perfect (I won't lie, the original dataset was very flawed). To fully fix this would require a very time-consuming manual edition, so you can consider it a WIP. |
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If you want to support me, you can [here](https://ko-fi.com/adamcodd). |
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## Token Distribution Analysis |
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We analyzed the token count distribution of the dataset using the LLAMA-3 tokenizer for the following Alpaca prompt: |
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``` |
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"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. |
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### Instruction: |
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{} |
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### Input: |
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{} |
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### Response: |
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{}""" |
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``` |
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### Key Statistics |
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- **Minimum tokens**: 164 |
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- **Maximum tokens**: 3,285 |
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- **Median (50th percentile)**: 274 tokens |
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### Decile Distribution |
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```ascii |
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| Percentile | Token Count | |
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|------------|-------------| |
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| 10% | 192 | |
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| 20% | 209 | |
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| 30% | 228 | |
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| 40% | 249 | |
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| 50% | 274 | |
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| 60% | 302 | |
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| 70% | 337 | |
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| 80% | 386 | |
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| 90% | 467 | |
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| 100% | 3,285 | |
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``` |
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### Interpretation |
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1. **Range**: The dataset contains prompts ranging from 164 to 3,285 tokens. |
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2. **Central Tendency**: The median token count is 274, meaning half of the prompts have 274 tokens or fewer. |
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3. **Distribution**: |
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- 90% of prompts have 467 tokens or fewer. |
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- There's a notable jump from the 90th percentile (467 tokens) to the maximum (3,285 tokens), suggesting some outliers with very high token counts. |
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4. **Implications for Training**: |
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- A sequence length of 400-500 tokens would cover the majority of prompts. |
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- Special handling may be needed for outliers with high token counts (e.g., truncation or splitting). |
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## Acknowledgment and citation |
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```bibtex |
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@inproceedings{bien-etal-2020-recipenlg, |
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title = "{R}ecipe{NLG}: A Cooking Recipes Dataset for Semi-Structured Text Generation", |
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author = "Bie{\'n}, Micha{\l} and |
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Gilski, Micha{\l} and |
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Maciejewska, Martyna and |
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Taisner, Wojciech and |
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Wisniewski, Dawid and |
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Lawrynowicz, Agnieszka", |
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booktitle = "Proceedings of the 13th International Conference on Natural Language Generation", |
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month = dec, |
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year = "2020", |
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address = "Dublin, Ireland", |
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publisher = "Association for Computational Linguistics", |
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url = "https://www.aclweb.org/anthology/2020.inlg-1.4", |
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pages = "22--28", |
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} |
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``` |
