CmdCody
/

nllb-deu-moo-v2

@@ -1,201 +1,73 @@
 ---
 library_name: transformers
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 ---
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 ---
 library_name: transformers
+license: cc-by-nc-4.0
+language:
+- de
+- frr
+pipeline_tag: translation
+base_model: facebook/nllb-200-distilled-600M
 ---
+# Model Card for nllb-deu-moo-v2
+This is an [NLLB-200-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) model fine-tuned for translating between
+German and the Northern Frisian dialect Mooring following [this great blogpost](https://cointegrated.medium.com/a37fc706b865).
 ## Model Details
 ### Model Description
+- **Language(s) (NLP):** Northern Frisian, German
+- **License:** Commons Attribution Non Commercial 4.0
+- **Finetuned from model:** NLLB-200-600M
 ## How to Get Started with the Model
+How to use the model:
+```python
+!pip install transformers>=4.38
+tokenizer = NllbTokenizer.from_pretrained("CmdCody/nllb-deu-moo-v2")
+model = AutoModelForSeq2SeqLM.from_pretrained("CmdCody/nllb-deu-moo-v2")
+model.cuda()
+def translate(text, tokenizer, model, src_lang='frr_Latn', tgt_lang='deu_Latn', a=32, b=3, max_input_length=1024, num_beams=4, **kwargs):
+    tokenizer.src_lang = src_lang
+    tokenizer.tgt_lang = tgt_lang
+    inputs = tokenizer(text, return_tensors='pt', padding=True, truncation=True, max_length=max_input_length)
+    result = model.generate(
+        **inputs.to(model.device),
+        forced_bos_token_id=tokenizer.convert_tokens_to_ids(tgt_lang),
+        max_new_tokens=int(a + b * inputs.input_ids.shape[1]),
+        num_beams=num_beams,
+        **kwargs
+    )
+    return tokenizer.batch_decode(result, skip_special_tokens=True)
+translate("Ik boog önj Naibel." tokenizer=tokenizer, model=model)
+```
 ## Training Details
 ### Training Data
+The training data consists of
+["Rüm Hart"](https://www.nordfriiskfutuur.eu/fileadmin/Content/Nordfriisk_Futuur/E-Books/N._A._Johannsen__Ruem_hart.pdf)
+published by the Nordfriisk Instituut. It was split and cleaned up, partially manually, resulting in 5178 example sentences.
 ### Training Procedure
+The training loop was implemented as described in [this article](https://cointegrated.medium.com/a37fc706b865).
+The model was trained for 5 epochs of 1000 steps each using a batch size of 16 using a Google GPU via a Colab notebook.
+Each epoch took roughly 30 minutes to train.
+The BLEU score was calculated on a set of 177 sentences taken from other sources.
 #### Metrics
+| Epochs  | Steps  | BLEU Score frr -> de  | BLEU Score de -> frr |
+|---------|--------|-----------------------|----------------------|
+| 1       | 1000   | 35.86  | 35.68  |
+| 2       | 2000   | 40.76  | 42.25  |
+| 3       | 3000   | 42.18  | 46.48  |
+| 4       | 4000   | 41.01  | 45.15  |
+| 5       | 5000   | 44.74  | 47.48  |