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README.md

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+---
+license: mit
+task_categories:
+  - text-classification
+language:
+  - en
+  - multilingual
+tags:
+  - abstract-detection
+  - scientific-text
+  - quality-filtering
+  - pubverse
+size_categories:
+  - 1K<n<10K
+---
+
+# Abstract Archon Training Data
+
+Binary classification dataset for detecting whether a text is a real scientific research abstract or non-abstract content (figure captions, supplementary material references, author bylines, journal metadata, HTML artifacts, taxonomy stubs).
+
+## Dataset Details
+
+- **Positive examples (label=1)**: 2,000 real abstracts randomly sampled from a 198M publication database (publications not flagged by any quality filter, length >= 200 characters)
+- **Negative examples (label=0)**: ~2,000 non-abstract texts from various garbage categories:
+  - `figure_table_caption`: Figure and table captions misidentified as abstracts
+  - `journal_article_scrape`: Scraped article metadata (titles, access info, author lists)
+  - `html_heavy_text`: Content with substantial HTML markup
+  - `author_byline`: Author affiliations and bylines
+  - `moesm_title`: Electronic supplementary material descriptions
+  - `supplementary_content`: Supplementary material references
+  - `taxonomy_stub`: Taxonomic database stubs
+
+## Format
+
+NDJSON with fields:
+- `text`: First 500 characters of the abstract field
+- `label`: 1 (real abstract) or 0 (garbage/non-abstract)
+- `source`: Category label (e.g., `positive_real_abstract`, `negative_figure_table_caption`)
+- `source_id`: OpenAlex-style source identifier
+
+## Data Curation
+
+Negatives were manually curated to remove misclassified entries. The `html_heavy` category was entirely removed after review showed nearly all entries were real abstracts with minor HTML entity artifacts. The `supplementary_content` category was filtered to retain only entries that begin with supplementary material indicators (e.g., "Figure S1", "Additional file", "Supplementary dataset").
+
+## Intended Use
+
+Training a lightweight binary classifier (Potion-32M embeddings + LogisticRegression) as a quality gate for large-scale scientific publication databases. The classifier identifies non-abstract text that has been incorrectly stored in abstract fields.
+
+## Source
+
+Sampled from a PostgreSQL database of ~198M publications aggregated from OpenAlex, PubMed, arXiv, bioRxiv, and medRxiv. Part of the [PubVerse](https://github.com/jimnoneill/pubverse) project.