nllb-200-1.3b-kbd-pretrain

NLLB-200 1.3B Pre-trained for Kabardian Translation

Model Details

• Model Name: nllb-200-1.3b-kbd-pretrain
• Base Model: NLLB-200 1.3B
• Model Type: Translation
• Language(s): Kabardian and others from NLLB-200 (200 languages)
• License: CC-BY-NC (inherited from base model)
• Developer: panagoa (fine-tuning), Meta AI (base model)
• Last Updated: January 23, 2025
• Paper: NLLB Team et al, No Language Left Behind: Scaling Human-Centered Machine Translation, Arxiv, 2022

Model Description

This model is a pre-trained adaptation of the NLLB-200 (No Language Left Behind) 1.3B parameter model that has been specifically optimized to improve translation capabilities for the Kabardian language (kbd). The base NLLB-200 model was developed by Meta AI and supports 200 languages, with this variant specifically adjusted for Kabardian language translation tasks.

Intended Uses

• Machine translation to and from Kabardian
• NLP applications involving the Kabardian language
• Research on low-resource language translation
• Cultural and linguistic preservation efforts for the Kabardian language

Training Data

This model has been pre-trained building upon the original NLLB-200 model, which used parallel multilingual data from various sources and monolingual data constructed from Common Crawl. The specific additional pre-training for Kabardian likely involved specialized Kabardian language resources.

The original NLLB-200 model was evaluated using the Flores-200 dataset.

Performance and Limitations

• As a pre-trained model, this version is intended to be further fine-tuned for specific translation tasks
• Inherits limitations from the base NLLB-200 model:
  • Not intended for production deployment (research model)
  • Not optimized for domain-specific texts (medical, legal, etc.)
  • Not designed for document translation (optimized for single sentences)
  • Training limited to input sequences not exceeding 512 tokens
  • Translations cannot be used as certified translations
• May have additional limitations when handling specific cultural contexts, dialectal variations, or specialized terminology in Kabardian

Usage Example

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model_name = "panagoa/nllb-200-1.3b-kbd-pretrain"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example: Translating to Kabardian
src_lang = "eng_Latn"  # English
tgt_lang = "kbd_Cyrl"  # Kabardian in Cyrillic script

text = "Hello, how are you?"
inputs = tokenizer(f"{src_lang}: {text}", return_tensors="pt")
translated_tokens = model.generate(
    **inputs, 
    forced_bos_token_id=tokenizer.lang_code_to_id[tgt_lang],
    max_length=30
)
translation = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
print(translation)

Ethical Considerations

As noted for the base NLLB-200 model:

• This work prioritizes human users and aims to minimize risks transferred to them
• Translation access for low-resource languages can improve education and information access but could potentially make groups with lower digital literacy vulnerable to misinformation
• Despite extensive data cleaning, personally identifiable information may not be entirely eliminated from training data
• Mistranslations could have adverse impacts on those relying on translations for important decisions

Caveats and Recommendations

• The base model was primarily tested on the Wikimedia domain with limited investigation on other domains
• Supported languages may have variations that the model does not capture
• Users should make appropriate assessments for their specific use cases
• This pre-trained model is part of a series of models specifically focused on Kabardian language translation
• For production use cases, consider the fully fine-tuned versions (v0.1, v0.2) rather than this pre-trained version

Additional Information

This model is part of a collection of NLLB models fine-tuned for Kabardian language translation developed by panagoa.