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--- |
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license: mit |
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task_categories: |
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- text-classification |
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language: |
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- en |
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tags: |
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- amazon |
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- products |
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- binary |
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- text |
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pretty_name: Amazon Scrape 4 llm |
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size_categories: |
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- n<1K |
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configs: |
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- config_name: phones |
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data_files: |
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- split: train |
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path: data/phones.csv |
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- config_name: laptops |
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data_files: |
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- split: train |
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path: data/laptops.csv |
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--- |
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# Amazon Scrape 4 llm |
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## Purpose? |
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Feed an LLM raw html to identify products from an ecommerce platform.\ |
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These datasets contain the extracted innerTexts of all HTML nodes from different ecommerce product pages.\ |
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The cleaning process significantly reduces the token size from ex: 450k -> 6k |
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## Quickstart |
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```py |
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from datasets import load_dataset |
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data_train = load_dataset("timashan/amazon-scrape-4-llm", "phones") |
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data_test = load_dataset("timashan/amazon-scrape-4-llm", "laptops") |
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``` |
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### JS snippet used for cleansing |
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```js |
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const allowedTags = sanitizeHtml.defaults.allowedTags; |
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allowedTags.splice(allowedTags.indexOf("a"), 1); |
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convert(sanitizeHtml(document.body.innerHTML, { allowedTags })); |
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``` |
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