french-serer-transformer-scratch

French–Serer Transformer from Scratch

Part of a benchmark of six configurations for French→Serer neural machine translation. Serer is a critically low-resource Niger-Congo language (~1.2M speakers, Senegal/Gambia), phylogenetically close to the well-resourced Wolof.

Model summary

• Experiment ID: F
• Kind: final_translation_model
• Direction: French → Serer
• Base model: none — Transformer encoder-decoder trained from scratch (no pretrained weights, custom SentencePiece tokenizer)
• Best checkpoint: french_serer_transformer_scratch-epoch=23-val_bleu=11.9665.ckpt
• Random seed: 42

Hyperparameters

• LR: 0.0003
• NUM_EPOCHS: 30
• D_MODEL: 128
• N_ENCODER_LAYERS: 3
• N_HEADS: 4
• DROPOUT: 0.3

This model has no Atlantic-family prior (its pretraining does not include Wolof or any close relative of Serer), unlike the NLLB-based configurations in this project. In our evaluation, this configuration was judged the most linguistically reliable by a native Serer-speaking expert despite a lower BLEU score than NLLB-based alternatives — see the paper for the full BLEU/quality decorrelation analysis.

Intended use

Research on French-to-Serer machine translation on a corpus that is ~90% religious (Bible) register, ~10% educational glossaries, primarily Siin dialect. Not validated for legal, medical, emergency, or fully autonomous publication use. Private repository — not intended for public deployment in its current state.

Evaluation

Evaluated on the held-out test split (2890 sentence pairs, SHA-256 of the split: 01d14d982a3c0cce172b5099e2db064d05bdef89677fe72a45f56715d6364ee2). Metrics were computed with the project's own evaluation scripts (not copied from the manuscript without independent reproduction); the training and evaluation code is kept in a private repository, available on request.

Training data and rights

Parallel corpus of 23113 train / 2889 val / 2890 test French–Serer sentence pairs, built primarily from religious texts (Bible, ~90%) and educational glossaries (~10%), predominantly Siin dialect. Preprocessing: Unicode normalization, exact-duplicate removal, length-ratio filtering (1:3–3:1). Document-level splitting was not possible (no document identifiers available); the split is at the sentence level with a fixed seed. Full provenance, licensing, and consent documentation are kept in a private dataset card, available on request, prior to any public release.