| license: mit | |
| task_categories: | |
| - feature-extraction | |
| - text-classification | |
| language: | |
| - en | |
| tags: | |
| - news | |
| - embeddings | |
| - semantic-search | |
| size_categories: | |
| - 100K<n<1M | |
| # News Embeddings Dataset | |
| This dataset contains news article embeddings generated using the [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) model. | |
| ## Source | |
| The original news articles were sourced from the [Global News Dataset on Kaggle](https://www.kaggle.com/datasets/everydaycodings/global-news-dataset?resource=download). | |
| ## Dataset Structure | |
| Each row contains: | |
| - `article_id`: Unique identifier for the article from the source dataset | |
| - `text`: Concatenation of title and description (separated by `\n---\n`) | |
| - `embedding`: 1024-dimensional embedding vector | |
| ## Embedding Model | |
| - **Model**: Qwen/Qwen3-Embedding-0.6B | |
| - **Prompt**: "Instruct: Given a news article summary, generate a semantic embedding that captures its key features and characteristics \nArticle:" | |
| ## Usage | |
| ```python | |
| import polars as pl | |
| from huggingface_hub import hf_hub_download | |
| # Download the parquet file | |
| path = hf_hub_download(repo_id="live-wire/news_embeddings", filename="embed.parquet", repo_type="dataset") | |
| df = pl.read_parquet(path) | |
| ``` | |