Bibliographic record
Abstract
A monolingual dataset built for Tigrinya language modeling. To the best of our knowledge, this is the largest dataset for Tigrinya of its kind. The data was collected from various sources across the web including news, blogs, and books. The largest portion of the data, ~75%, comes from over 2150 issues of the Haddas Ertra newspaper and other magazines published by www.shabait.com. Data Statistics: Total size: ~0.5GB Around 40 million tokens Over 2 million lines 367 unique characters Train split: 98%, 1.97 million lines Validation split: 2%, 43k lines We have done a light-weight cleanup of the data: - Removal of Tigrinya text with legacy and non-standard encoding systems - Normalization of punctuation and special characters - Removal of redundant white spaces and empty lines - Rejoining or fixing broken sentences when possible - Removal of foreign words We avoid applying any form of tokenization, extensive cleanup, and preprocessing operations in order not to take away potentially useful information, those decisions are left to the use-case researchers or developers. This dataset is shared solely to advance research on natural language processing for Tigrinya. While the dataset authors do not claim any copyright on the content, some of the original sources may do. To use the content for commercial purposes or other forms of redistribution of the data, permission shall be acquired from the original owners, mainly shabait.com.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.056 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".