| --- |
| dataset_info: |
| features: |
| - name: post |
| dtype: string |
| - name: newsgroup |
| dtype: string |
| - name: embedding |
| sequence: float64 |
| - name: map |
| sequence: float64 |
| splits: |
| - name: train |
| num_bytes: 129296327 |
| num_examples: 18170 |
| download_size: 102808058 |
| dataset_size: 129296327 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| --- |
| |
| # Dataset Card for 20-Newsgroups Embedded |
|
|
| This provides a subset of 20-Newsgroup posts, along with sentence embeddings, and a dimension reduced 2D data map. |
| This provides a basic setup for experimentation with various neural topic modelling approaches. |
|
|
| ## Dataset Details |
|
|
| ### Dataset Description |
|
|
| This is a dataset containing posts from the classic 20-Newsgroups dataset, along with sentence embeddings, and a dimension reduced 2D data map. |
|
|
| Per the [source](http://qwone.com/~jason/20Newsgroups/): |
| > The 20-Newsgroups dataset is a collection of approximately 20,000 newsgroup documents, partitioned (nearly) evenly across 20 different newsgroups. |
| > As far as in known it was originally collected by Ken Lang, probably for his Newsweeder: Learning to filter netnews paper. |
| > The 20 newsgroups collection has become a popular data set for experiments in text applications of machine learning techniques, |
| > such as text classification and text clustering. |
|
|
| This has then been enriched with sentence embeddings via sentence-transformers using the `all-mpnet-base-v2` model. Further enrichment is |
| provided in the form of a 2D representation of the sentence embeddings generated using UMAP. |
|
|
| - **Curated by:** Leland McInnes |
| - **Language(s) (NLP):** English |
| - **License:** Public Domain |
|
|
| ### Dataset Sources |
|
|
| The post and newsgroup data was collected using the `sckit-learn` function `fetch_20newsgroups` and then processed to exclude very short |
| and excessively long posts in the following manner: |
|
|
| ```python |
| newsgroups = sklearn.datasets.fetch_20newsgroups(subset="all", remove=("headers", "footers", "quotes")) |
| useable_content = np.asarray([len(x) > 8 and len(x) < 16384 for x in newsgroups.data]) |
| documents = [ |
| doc |
| for doc, worth_keeping in zip(newsgroups.data, useable_content) |
| if worth_keeping |
| ] |
| newsgroup_categories = [ |
| newsgroups.target_names[newsgroup_id] |
| for newsgroup_id, worth_keeping in zip(newsgroups.target, useable_content) |
| if worth_keeping |
| ] |
| ``` |
|
|
| - **Repository:** The original source datasets can be found at [http://qwone.com/~jason/20Newsgroups/](http://qwone.com/~jason/20Newsgroups/) |
|
|
| ## Uses |
|
|
| This datasets is intended to be used for simple experiments and demonstrations of topic modelling and related tasks. |
|
|
|
|
| #### Personal and Sensitive Information |
|
|
| This data may contain personal information that was posted publicly to NNTP servers in the mid 1990's. It is not believed to contain |
| any senstive information. |
|
|
| ## Bias, Risks, and Limitations |
|
|
| This dataset is a product of public discussion forums in the 1990s. As such it contains debate, potentially inflammatory and/or |
| derogatory language, etc. It does not provide a representative sampling of opinion from the era. This data should only be used |
| for experiments or demonstration and educational purposes. |
|
|
