Translation
Transformers
Safetensors
m2m_100
text2text-generation
language
linguistics
low-resource
tatoeba
nllb
machine-translation
gronings
Instructions to use Tom9358/nllb-tatoeba-gos-nld-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tom9358/nllb-tatoeba-gos-nld-v1 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="Tom9358/nllb-tatoeba-gos-nld-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Tom9358/nllb-tatoeba-gos-nld-v1") model = AutoModelForSeq2SeqLM.from_pretrained("Tom9358/nllb-tatoeba-gos-nld-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add license, add headers to readme for better readability
Browse files
README.md
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- nllb
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- machine-translation
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- gronings
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---
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Moi!
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The github repo contains some BLEU and ChrF score plots, but I haven't thoroughly investigated the performance by means of them and am hesitant to claim any particular general translation performance for this version.
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Fortunately, I am a linguist and speaker of Gronings so I could carry out evaluation by expert's eyeball.
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I consider this interesting enough for a public PoC, so I decided to publish.
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It is certainly possible to create a better model by means of (synthetic) data addition and hyperparameter optimization. I have not employed backtranslation either yet.
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Update 10 September 2025: I've updated the code to the latest version of `transformers` so that it can immediately be used by anyone without any tokenizer black magic needed. Also about 500 more parallel nld-gos sentences were added to the training data.
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Only the additional Gronings language token needs to be added to the tokenizer at initialization, then everything should work.
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Update 16 January 2026: Add a few hundred more sentences and allow longer sentences in training loop (at the cost of longer training time).
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See https://github.com/tom9358/nllb-tryout for everything (code, more documentation and references) except the model itself and training data.
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Check out the dedicated Huggingface space to try out the model! Find it here: https://huggingface.co/spaces/Tom9358/gos_gronings_translate
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In case for some reason the HF space stops working, here is another (albeit slower) way to try out the model:
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https://colab.research.google.com/drive/1b5dn3VT4fvOBKly1CIx4Qwo59GDM1H-M
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The code there is also a minimal example of how to use this model.
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Don't hesitate to contact me if anything comes up!
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- nllb
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- machine-translation
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- gronings
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license: cc-by-nc-4.0
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library_name: transformers
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---
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# NLLB Tatoeba finetune Gronings - Dutch v1
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Moi!
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This is a finetuned NLLB model for Gronings (gos) trained on sentence pairs from [Tatoeba](https://tatoeba.org/). Consider this an early beta release!
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## Quality / evaluation
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The github repo contains some BLEU and ChrF score plots, but I haven't thoroughly investigated the performance by means of them and am hesitant to claim any particular general translation performance for this version.
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Fortunately, I am a linguist and speaker of Gronings so I could carry out evaluation by expert's eyeball.
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I consider this interesting enough for a public PoC, so I decided to publish.
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It is certainly possible to create a better model by means of (synthetic) data addition and hyperparameter optimization. I have not employed backtranslation either yet.
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## Updates
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Update 10 September 2025: I've updated the code to the latest version of `transformers` so that it can immediately be used by anyone without any tokenizer black magic needed. Also about 500 more parallel nld-gos sentences were added to the training data.
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Only the additional Gronings language token needs to be added to the tokenizer at initialization, then everything should work.
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Update 16 January 2026: Add a few hundred more sentences and allow longer sentences in training loop (at the cost of longer training time).
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## Links
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See https://github.com/tom9358/nllb-tryout for everything (code, more documentation and references) except the model itself and training data.
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Check out the dedicated Huggingface space to try out the model! Find it here: https://huggingface.co/spaces/Tom9358/gos_gronings_translate
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In case for some reason the HF space stops working, here is another (albeit slower) way to try out the model:
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https://colab.research.google.com/drive/1b5dn3VT4fvOBKly1CIx4Qwo59GDM1H-M
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Don't hesitate to contact me if anything comes up!
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