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--- |
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license: mit |
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task_categories: |
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- question-answering |
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pretty_name: Every Prompt |
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size_categories: |
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- 1M<n<10M |
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multilinguality: |
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- multilingual |
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--- |
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## Every Prompt |
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Every Prompt is a data-driven approach to mining instructions from the web. |
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It contains over a million FAQs and HowTos from around the world in a structured format. |
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It also has basic pre-processing to calculate the length of the useful text and identify the language of that text with the help of [GCLD3](https://github.com/google/cld3) |
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It relies on the [Web Data Commons](http://webdatacommons.org) dataset (from October 2022) to find the seed list of sites with [**HowTo**](https://schema.org/HowTo) and [**FAQPage**](https://schema.org/FAQPage) items. |
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The general pipeline looks like this: |
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* Download 1.6TB of structured data from webdatacommons to identify the pages with the structured data we need (wget/parallel). That gives us 1,985,925 seed pages |
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* Crawls the seed pages and tries to extract structured data using [extruct](https://pypi.org/project/extruct/#description) package. That left around 1,358,638 pages which are alive and well-formed. |
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* Extracts only the relevant structured data of the HowTo/FAQPage type with the help of jmespath. That boils down to 1,266,926 json documents. |
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* Extracts the textual information out of the structure to identify the text's language, the textual data's length, and the text/data ratio. |
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You can use the resulting dataset by filtering for the language and amount of the text. You need to convert the structured data into instructions yourself. |
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You'll need to apply extra cleansing/evaluation of the instructions you've got because, you know, the internet is still full of crap. |
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## Recreating the results |
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1. Clone the repo without the LFS files |
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2. Install requirements from `requirements.txt` |
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3. Install `pv` and `parallel` |
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4. Run `bin/get_seed_urls.sh` to filter urls of interest out of 1.6TB of compressed data. Don't worry about disk space. Worry about the traffic |
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5. Run scrapy spider like this `scrapy crawl webdatacommons_org -s WEB_DATA_COMMONS=web_data_commons_urls_sample.txt -L INFO -o webdatacommons.jsonlines` with `WEB_DATA_COMMONS` pointing to the list of seed URLs from step 4. |
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6. Run `python extract_relevant_structured_data.py --num-threads 12 webdatacommons.jsonlines relevant.jsonlines.bz2` |
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7. Run `python export_structured_data.py relevant.jsonlines.bz2 extruct_out.jsonlines.bz2` to obtain the final version of the dataset |
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## License |
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**Code** of the project has an MIT license. |
