Instructions to use JosueG/adja-nmt-nllb-600m-forward-r10ks4k-seed42 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JosueG/adja-nmt-nllb-600m-forward-r10ks4k-seed42 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="JosueG/adja-nmt-nllb-600m-forward-r10ks4k-seed42")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("JosueG/adja-nmt-nllb-600m-forward-r10ks4k-seed42") model = AutoModelForSeq2SeqLM.from_pretrained("JosueG/adja-nmt-nllb-600m-forward-r10ks4k-seed42", device_map="auto") - Notebooks
- Google Colab
- Kaggle
NLLB-600M French -> Adja
This is the thesis-final French-to-Adja machine translation checkpoint for the May 2026 CS thesis release. It is one of the two NLLB models used to make the French-Adja MT work inspectable and reproducible.
Thesis Role
This model is the forward MT artifact: French source text to Adja target text. It supports the thesis experiments on grammar-guided dataset construction, low-resource NMT fine-tuning, and the speech-to-text-to-speech pipeline.
Model And Data
- Task: machine translation, French -> Adja
- Base model:
facebook/nllb-200-distilled-600M - Direction:
fra_Latn->aj_Latn - Training data: French-Adja thesis corpus lineage, with the public
canonical MT reference at
JosueG/french-adja-parallel-corpus - Release repo:
FrejusGdm/cs-thesis-may-2026
Headline Result
The seed-42 NLLB-600M full/module runs are the strongest MT baseline in the release. The public GitHub result tables report about 21-22 BLEU and 29 chrF on the held-out MT evaluation for the full-corpus NLLB setting.
See:
results/mt/summary/all_results.csvresults/mt/README_thesis_qualitative_decode.md
Limitations
- Adja remains low-resource; outputs should be reviewed before community, educational, or publication use.
- Metrics are sensitive to corpus split, normalization, and reference coverage.
- The model uses the release tokenizer/config; load this repo directly so the Adja language token and generation settings match the checkpoint.
Citation
If you use this model, cite:
Josue Godeme. 2026. CS Thesis May 2026: French-Adja MT and Adja Speech Experiments. https://github.com/FrejusGdm/cs-thesis-may-2026
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Model tree for JosueG/adja-nmt-nllb-600m-forward-r10ks4k-seed42
Base model
facebook/nllb-200-distilled-600M