slone
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nllb-with-myv-v2024

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 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+license: cc-by-nc-4.0
+language:
+- myv
+- ru
+- ar
+- en
+- et
+- fr
+- de
+- kk
+- ch
+- zh
+- mn
+- es
+- tr
+- uk
+- uz
+base_model:
+- facebook/nllb-200-distilled-600M
+datasets:
+- slone/myv_ru_2022
+- slone/e-mordovia-articles-2023
+pipeline_tag: translation
 ---
+# Model Card for NLLB-with-myv-v2024 (a translation model for Erzya)
+This is a version of the [nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) machine translation model
+with one added language: Erzya (the new language code is `myv_Cyrl`).
+It can probably translate from all 202 NLLB languages, but it fine-tuned with the focus on Erzya, Russian, and, to a lesser extent,
+on Arabic, English, Estonian, Finnish, French, German, Kazakh, Mandarin, Mongolian, Spanish, Turkish, Ukrainian, and Uzbek.
 ## Model Details
 ### Model Description
+- **Developed by:** Isai Gordeev, Sergey Kuldin and David Dale
+- **Model type:** Encoder-decoder transformer
+- **Language(s) (NLP):** Erzya, Russian, and all the 202 NLLB languages.
+- **License:** CC-BY-NC-4.0
+- **Finetuned from model:** [nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M)
 ### Model Sources [optional]
 <!-- Provide the basic links for the model. -->
+- **Repository:** will be published later
+- **Paper:** will be published later
+- **Demo:** https://lango.to/ (it is powered by a similar model)
 ## Uses
 ### Direct Use
+Translation between Erzya, Russian, and potentially other languages. The model seems to be SOTA for translating into Erzya.
 ### Out-of-Scope Use
+Translation between other NLLB languages, not inclusing Erzya as source or target.
 ## Bias, Risks, and Limitations
+The model is not producing the most fluent translations into Russian and other high-resourced languages.
+Its translations into Erzya seem to be better than anything else, but may still include inaccurate or ungrammatical translations,
+so they should be always manually reviewed before any high-responsibility use.
 ### Recommendations
+Please contact the authors for any substantial recommendation.
 ## How to Get Started with the Model
+See the NLLB generation code: https://huggingface.co/docs/transformers/v4.44.2/en/model_doc/nllb#generating-with-nllb.
 ## Training Details
 ### Training Data
+- https://huggingface.co/datasets/slone/myv_ru_2022
+- https://huggingface.co/datasets/slone/e-mordovia-articles-2023
 ### Training Procedure
 #### Preprocessing [optional]
+The preprocessing code is adapted from  the Stopes repo of the NLLB team:
+https://github.com/facebookresearch/stopes/blob/main/stopes/pipelines/monolingual/monolingual_line_processor.py#L214
+It performs punctuation normalization, nonprintable character removal and Unicode normalization.
 #### Training Hyperparameters
+The tokenizer of the model was updated with 6209 new Erzya tokens. They were initialized with the average embeddings of the old tokens from which they are combined.
+- training regime: `fp32`
+- batch_size: 6
+- grad_acc_steps: 4
+- max_length: 128
+- optimizer: Adafactor
+- lr: 1e-4
+- clip_threshold=1.0
+- weight_decay: 1e-3
+- warmup_steps: 3_000 (with a linear warmup from 0)
+- training_steps: 220_000
+- weight_loss_coef: 100 (a coefficient for the additional penalty, MSE between the embeddings of old tokens and their values for NLLB-200)
+## Technical Specifications
 ### Model Architecture and Objective
+A standard encoder-decoder translation model with cross-entropy loss.
 ### Compute Infrastructure
+Google Colab with a T4 GPU.
+```
+pip install --upgrade sentencepiece transformers==4.40 datasets sacremoses editdistance sacrebleu razdel ctranslate2
+```
 ## Model Card Contact
+@cointegrated