ScarlettMagdaleno/ccnews_all-roberta-large-v1_1024

Dataset Card for ccnews_all-roberta-large-v1_dim1024

This dataset contains English news articles from the CCNEWS dataset along with their corresponding 1024-dimensional embeddings, precomputed using the sentence-transformers/all-roberta-large-v1 model.

Note: If you are only interested in the raw embeddings (without the associated text), a compact version is available in the related repository: ScarlettMagdaleno/ccnews-embeddings-dim1024.

Dataset Details

Dataset Description

Each entry in this dataset is stored in Apache Parquet format, split into multiple files for scalability. Each record contains two fields:

• 'article': The original news article text.

• 'embedding': A 1024-dimensional list representing the output of the sentence-transformers/all-roberta-large-v1 encoder.

• Curated by: Scarlett Magdaleno

• Language(s) (NLP): English

• License: Other (the dataset is a derivative of the CCNEWS dataset, which may carry its own license)

Dataset Sources

• Base Text Dataset: sentence-transformers/ccnews
• Embedding Model: sentence-transformers/all-roberta-large-v1

Dataset Creation

Curation Rationale

The dataset was created to enable fast and reproducible similarity search experiments, as well as to provide a resource where the relationship between the raw text and its embedding is explicitly retained.

Source Data

Data Collection and Processing

• Texts were taken from the CCNEWS dataset available on Hugging Face.
• Each article was passed through the encoder all-roberta-large-v1 from the sentence-transformers library.
• The resulting embeddings were stored along with the article in Parquet format for efficient disk usage and interoperability.

Who are the source data producers?

• The original texts come from English-language news websites.

• The embeddings were generated and curated by Scarlett Magdaleno.

• Repository: https://huggingface.co/datasets/ScarlettMagdaleno/ccnews_all-roberta-large-v1_dim1024

Uses

Direct Use

This dataset is suitable for:

• Training and evaluating similarity search models.
• Experiments involving semantic representation of news content.
• Weakly supervised learning using embeddings as targets or features.
• Benchmarks for contrastive or clustering approaches.

Out-of-Scope Use

• Not suitable for generative modeling tasks (no labels, no dialogues, no instructions).
• Does not include metadata such as timestamps, URLs, or categories.

Dataset Structure

Each Parquet file contains a table with two columns:

• article (string): The raw article text.
• embedding (list[float]): A list of 1024 float values representing the semantic embedding.

Format

• Storage format: Apache Parquet.
• Total records: same as CCNEWS — approximately 614,664 articles.
• Split: the dataset is divided into multiple parquet files for better loading performance.

Example Record

{
  "article": "U.S. President signs new environmental policy...",
  "embedding": [0.023, -0.117, ..., 0.098]  # 1024 values
}