Towards Amazigh Word Embedding: Corpus Creation and Word2Vec Models Evaluations
Bibliographic record
Abstract
Distributed representations of words in a vector space help learning algorithms to model semantic notions of word similarity and distances in sentences.Most of the existing researches have been done on the Latin, Arabic, and other language, while the Amazigh language is ignored.In this paper, we try to build a first model word embeddings for Amazigh language and describe the steps needed to build it.Therefore, we implement a Word2Vec that a combination of two techniques -CBOW (Continuous bag of words) and Skip-gram to transform words written in Tifinagh to vector form.To obtain the highest performance, we evaluate two parameters of Word2Vec include Word2Vec model architecture and vector dimension.This evaluation process was implemented towards our proposed corpus collected on Amazigh websites for different domains.The result shows that the highest accuracy values are obtained under the combination of CBOW model and 300 dimensional vector.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".