metadata

language:
  - en
license: mit
task_categories:
  - text-generation
  - feature-extraction
pretty_name: AI/Technology Articles
tags:
  - temporal series data
  - language data
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
dataset_info:
  features:
    - name: id
      dtype: int64
    - name: year
      dtype: int64
    - name: title
      dtype: string
    - name: url
      dtype: string
    - name: text
      dtype: string
  splits:
    - name: train
      num_bytes: 123475673
      num_examples: 2429
  download_size: 25153621
  dataset_size: 123475673

AI/Tech Dataset

This dataset is a collection of AI/tech articles scraped from the web.

It's hosted on HuggingFace Datasets, so it is easier to load in and work with.

To load the dataset

1. Install HuggingFace Datasets

pip install datasets

2. Load the dataset

from datasets import load_dataset

dataset = load_dataset("siavava/ai-tech-articles")

# optionally, convert it to a pandas dataframe:
df = dataset["train"].to_pandas()

You do not need to clone this repo. HuggingFace will download the dataset for you, the first time that you load it, and cache it locally so it does not need to re-download it again (unless it detects a change upstream).

File Structure

analytics.ipynb - Notebook containing some details about the dataset.
example.ipynb - A minimal notebook that loads in the dataset and converts to Pandas.
raw.csv - The raw data, in CSV format.
data/*.parquet- compressed parquet containing the data.
For raw text files, see the scraper repo on GitHub.