Datasets:
| 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. | |