---
license: mit
task_categories:
  - text-classification
language:
  - en
tags:
  - amazon
  - products
  - binary
  - text
pretty_name: Amazon Scrape 4 llm
size_categories:
  - n<1K
configs:
  - config_name: phones
    data_files:
      - split: train
        path: data/phones.csv
  - config_name: laptops
    data_files:
      - split: train
        path: data/laptops.csv
---

# Amazon Scrape 4 llm

## Purpose?

Feed an LLM raw html to identify products from an ecommerce platform.\
These datasets contain the extracted innerTexts of all HTML nodes from different ecommerce product pages.\
The cleaning process significantly reduces the token size from ex: 450k -> 6k

## Quickstart

```py
from datasets import load_dataset

data_train = load_dataset("timashan/amazon-scrape-4-llm", "phones")
data_test = load_dataset("timashan/amazon-scrape-4-llm", "laptops")
```

### JS snippet used for cleansing

```js
const allowedTags = sanitizeHtml.defaults.allowedTags;
allowedTags.splice(allowedTags.indexOf("a"), 1);
convert(sanitizeHtml(document.body.innerHTML, { allowedTags }));
```