panagoa/nllb-200-1.3b-kbd-v0.2

NLLB-200 1.3B Advanced Fine-tuning for Kabardian Translation (v0.2)

Model Details

• Model Name: nllb-200-1.3b-kbd-v0.2
• Base Model: NLLB-200 1.3B
• Model Type: Text2Text Generation
• 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: February 28, 2025
• Paper: NLLB Team et al, No Language Left Behind: Scaling Human-Centered Machine Translation, Arxiv, 2022

Model Description

This model represents the latest iteration (v0.2) in panagoa's series of NLLB-200 adaptations for Kabardian language. Building upon the previous v0.1 version, this model incorporates additional fine-tuning and improvements to enhance translation quality, accuracy, and fluency specifically for the Kabardian language. The model is classified as a Text2Text Generation model, potentially offering broader text generation capabilities beyond direct translation.

Intended Uses

• High-quality machine translation to and from Kabardian
• Text generation and paraphrasing in Kabardian
• Cross-lingual information access and content creation
• NLP applications and research for the Kabardian language
• Cultural and linguistic preservation efforts
• Educational resources and accessibility tools for Kabardian speakers
• Documentation and digital content creation in Kabardian

Training Data

This enhanced model (v0.2) has been fine-tuned with additional training data and potentially improved techniques compared to v0.1, building upon the strong foundation of the NLLB-200 architecture. The specific improvements likely focus on addressing limitations identified in earlier versions and expanding the model's capabilities for Kabardian language processing.

Performance and Limitations

• Improved translation and text generation performance for Kabardian language compared to previous versions
• Enhanced handling of Kabardian language nuances, idioms, and grammatical structures
• As the most recent version in the series, it represents the current state-of-the-art for this specific model family
• Still inherits some fundamental limitations from the base NLLB-200 model:
  • Research-oriented model not designed for critical production deployments
  • Performance may vary across different domains and contexts
  • Limited to input sequences not exceeding 512 tokens
  • Translations should not be used as certified translations without human review
• May still face challenges with highly specialized terminology, cultural nuances, or regional dialects

Usage Example

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model_name = "panagoa/nllb-200-1.3b-kbd-v0.2"
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 = "Welcome to our community. We are happy to share our culture and language with 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=50
)
translation = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
print(translation)

# Example: Translating from Kabardian
kbd_text = "Ди щӀыналъэм и дахагъэр пхуэӀуэтэщӀынукъым."
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=50
)
translation = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
print(translation)

Ethical Considerations

As noted for the base NLLB-200 model and applicable to this fine-tuned version:

• This work prioritizes human users and aims to minimize risks transferred to them
• Translation and text generation technologies for low-resource languages like Kabardian can significantly improve education, information access, and digital representation
• Enhanced language technologies can help preserve linguistic heritage and cultural knowledge
• Potential risks include:
  • Making groups with lower digital literacy vulnerable to misinformation
  • Potential reinforcement of biases present in training data
  • Mistranslations that could impact decision-making in important contexts
• Despite extensive data cleaning, personally identifiable information may not be entirely eliminated from training data

Caveats and Recommendations

• This model represents the current recommended version (v0.2) in the series
• Performance may still vary across different domains, dialects, and contexts
• While improved over previous versions, output should still be reviewed by fluent speakers for critical applications
• The model can be used for broader text generation tasks beyond direct translation
• For optimal results, clearly specify the source and target languages using the appropriate language codes
• Users working with specialized terminology or domain-specific content should validate outputs with subject matter experts

Additional Information

This model is part of panagoa's ongoing effort to improve NLP capabilities for the Kabardian language. As the latest version in this collection, it represents the current recommended model for Kabardian language translation and text generation tasks. For comparative analysis or specific requirements, earlier versions (pre-trained and v0.1) remain available.