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New Yama model offers efficient zero-shot classification for Indian languages

By PulseAugur Editorial · [1 sources] ·

A new open-weights model named Yama, with 586 million parameters, has been developed for zero-shot text classification, particularly for Indian languages and Hinglish. This model aims to provide a more efficient and cost-effective solution compared to fine-tuned classifiers or large language model calls for tasks like classifying customer messages. Yama demonstrated strong performance, achieving a score of 0.814 on 33 unseen tasks, outperforming other open zero-shot classifiers, with a significant portion of this improvement attributed to using a larger encoder. AI

IMPACT This model offers a more efficient approach to text classification for underrepresented languages, potentially lowering costs and improving response times for businesses operating in those regions.

RANK_REASON The cluster describes the release of a new open-weights model with specific performance metrics and capabilities. [lever_c_demoted from research: ic=1 ai=1.0]

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AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Yama model offers efficient zero-shot classification for Indian languages

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The cluster describes the release of a new open-weights model with specific performance metrics and capabilities. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Towards AI TIER_1 English(EN) · Latent AI Labs ·

    Before You Invent a New Architecture, Try a Bigger Encoder

    <h4><em>Looped transformers, targeted data, continued pretraining: each under 1 point. Swapping in a bigger encoder: +5.3. Lessons from Yama.</em></h4><p><em>Disclosure: this article was drafted with help from an AI assistant, and the charts were made with AI-assisted code from m…

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