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Datasets:
siavava
/
ai-tech-articles

Tasks:
Text Generation
Feature Extraction
Modalities:
Tabular
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
Tags:
temporal series data
language data
DOI:
Libraries:
Datasets
pandas
License:
Dataset card Viewer Files Community
1
ai-tech-articles
File size: 1,869 Bytes 
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---
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: 180820047
    num_examples: 17092
  download_size: 81702921
  dataset_size: 180820047
---

# 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()
```

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`](analytics.ipynb) - Notebook containing some details about the dataset.
- [`example.ipynb`](example.ipynb) - A minimal notebook that loads in the dataset and converts to Pandas.
- [`raw.csv`](raw.csv) - The raw data, in CSV format.
- `data/*.parquet`- 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.