---
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](https://huggingface.co/datasets/siavava/ai-tech-articles), so it is easier to load in and work with.

## To load the dataset

### 1. Install [HuggingFace Datasets](https://huggingface.co/docs/datasets/installation.html)

```bash
pip install datasets
```

### 2. Load the dataset

```python
from datasets import load_dataset

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

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

> [!NOTE]
> 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 so it does not need to re-download it again
> (unless it detects a change upstream).

## File Structure

- [analytics.ipynb](analytics.ipynb) - Notebook containing some details about the dataset and how to load it.
- [data/index.parquet](./index.csv) - compressed [parquet](https://www.databricks.com/glossary/what-is-parquet) containing the data.
- For raw text files, see the [scraper repo](https://github.com/siavava/scrape.hs) on GitHub.