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