Semantics Squad at BLP-2023 Task 1: Violence Inciting Bangla Text Detection with Fine-Tuned Transformer-Based Models
Bibliographic record
Abstract
This study investigates the application of Transformer-based models for violence threat identification.We participated in the BLP-2023 Shared Task 1 and in our initial submission, BanglaBERT large achieved 5 th position on the leader-board with a macro F1 score of 0.7441, approaching the highest baseline of 0.7879 established for this task.In contrast, the top-performing system on the leaderboard achieved an F1 score of 0.7604.Subsequent experiments involving m-BERT, XLM-RoBERTa base, XLM-RoBERTa large, BanglishBERT, BanglaBERT, and BanglaBERT large models revealed that BanglaBERT achieved an F1 score of 0.7441, which closely approximated the baseline.Remarkably, m-BERT and XLM-RoBERTa base also approximated the baseline with macro F1 scores of 0.6584 and 0.6968, respectively.A notable finding from our study is the under-performance by larger models for the shared task dataset, which requires further investigation.Our findings underscore the potential of transformer-based models in identifying violence threats, offering valuable insights to enhance safety measures on online platforms.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".