panagoa/nllb-200-1.3b-kbd-v0.1

NLLB-200 1.3B Fine-tuned for Kabardian Translation (v0.1)

Model Details

• Model Name: nllb-200-1.3b-kbd-v0.1
• 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 24, 2025
• Paper: NLLB Team et al, No Language Left Behind: Scaling Human-Centered Machine Translation, Arxiv, 2022

Model Description

This model is a fine-tuned version (v0.1) of the NLLB-200 (No Language Left Behind) 1.3B parameter model, specifically optimized for Kabardian language translation. It builds upon the pre-trained variant (panagoa/nllb-200-1.3b-kbd-pretrain) with further fine-tuning to enhance translation quality and accuracy for the Kabardian language. The model represents an early release in panagoa's series of Kabardian language translation models.

Intended Uses

• High-quality machine translation to and from Kabardian
• Cross-lingual information access for Kabardian speakers
• NLP applications and research for the Kabardian language
• Cultural and linguistic preservation efforts
• Educational tools and resources for the Kabardian community

Training Data

This model has been fine-tuned on specialized Kabardian language datasets, building upon the original NLLB-200 model which used parallel multilingual data from various sources. The fine-tuning process likely focused on improving translation quality specifically for Kabardian language pairs.

Performance and Limitations

• Improved translation performance for Kabardian language compared to the base NLLB-200 model
• As an early version (v0.1), it may not perform as well as later iterations (v0.2+)
• Inherits some limitations from the base NLLB-200 model:
  • Research model, not intended for critical production deployment
  • Not optimized for specialized domains (medical, legal, technical)
  • Designed for single sentences rather than long documents
  • Limited to input sequences not exceeding 512 tokens
  • Translations should not be used as certified translations
• May have challenges with regional dialects, specialized terminology, or culturally-specific expressions in Kabardian

Usage Example

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model_name = "panagoa/nllb-200-1.3b-kbd-v0.1"
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)

# Example: Translating from Kabardian
kbd_text = "Сэлам, дауэ ущыт?"
inputs = tokenizer(f"{tgt_lang}: {kbd_text}", return_tensors="pt")
translated_tokens = model.generate(
    **inputs, 
    forced_bos_token_id=tokenizer.lang_code_to_id[src_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 like Kabardian can improve education and information access
• Potential risks include making 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 model may perform inconsistently across different domains and contexts
• Performance on specialized Kabardian dialects may vary
• This version represents an early fine-tuning iteration (v0.1)
• For better performance, consider using later versions (v0.2+) if available
• Users should evaluate the model's output quality for their specific use cases
• Not recommended for mission-critical applications without human review

Additional Information

This model is part of a collection of NLLB models fine-tuned for Kabardian language translation developed by panagoa. For optimal performance, compare results with other models in the collection, particularly more recent versions.