AbdallahNasir/gender-mt-gemma2-2b-sft-fewshot-inv
The AbdallahNasir/gender-mt-gemma2-2b-sft-fewshot-inv is a 2.6 billion parameter language model, based on the Gemma2 architecture, fine-tuned for gender-specific machine translation tasks. It was trained using supervised fine-tuning (SFT) on TED talk gender pairs and no-change sentences, employing a 3-shot prompting strategy. This model is specialized in handling gender nuances in translation, making it suitable for applications requiring gender-aware language processing.
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Overview
The gender-mt-gemma2-2b-sft-fewshot-inv is a specialized language model developed by AbdallahNasir, built upon the Gemma2 architecture with 2.6 billion parameters and an 8192-token context length. This model has undergone supervised fine-tuning (SFT) specifically for gender-aware machine translation. Its training methodology involved using TED talk datasets, focusing on gender-paired sentences and sentences where no gender change was required, utilizing a 3-shot prompting approach.
Key Capabilities
- Gender-Specific Translation: Optimized to handle and differentiate between male, female, and neutral gender contexts in translation tasks.
- Few-Shot Learning: Leverages a 3-shot prompting strategy, demonstrating adaptability with limited examples.
- Gemma2 Architecture: Benefits from the foundational capabilities of the Gemma2 model family.
Good For
- Gender-aware Machine Translation: Ideal for applications requiring translations that accurately reflect or adapt to specific gender contexts.
- Bias Mitigation Research: Useful for researchers studying and mitigating gender bias in language models and translation systems.
- Specialized Language Processing: Suitable for tasks where understanding and generating gender-nuanced language is critical.