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Add license, add headers to readme for better readability

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@@ -18,11 +18,17 @@ tags:
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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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- I used sentence pairs from https://tatoeba.org/ to finetune an NLLB model for Gronings. Consider this an early beta version!
 
 
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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.
@@ -30,6 +36,8 @@ The model generally produces acceptable Gronings when the input language is Dutc
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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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@@ -39,6 +47,8 @@ Update 28 November 2025: Add another ~90 sentences. Upon further inspection, and
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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
@@ -80,6 +90,4 @@ translate("Dit is een testzin om te kijken of de code werkt.")
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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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-
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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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+
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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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+
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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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+
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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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+
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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!