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User seeks translation models that preserve proper nouns across 100+ languages

By PulseAugur Editorial · [1 sources] ·

A user on r/MachineLearning is seeking advice on the best text-to-text translation models for a project requiring translation of over 100 languages into English. They are encountering difficulties with preserving proper nouns like names, places, dates, and organizations, a problem that persists even with advanced NMT models like NLLB and LLMs such as Gemma 4 and Qwen 3 4B. The user is looking for solutions that can run locally on an RTX GPU, with models under 7B parameters, and is open to suggestions on better multilingual translation models, NER approaches, or decoding techniques. AI

RANK_REASON This is a user query on Reddit asking for advice on AI models, not a news announcement or research paper.

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

User seeks translation models that preserve proper nouns across 100+ languages

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0 / 100
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Newsworthiness bucket
Meme
This is a user query on Reddit asking for advice on AI models, not a news announcement or research paper.
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
model release, product
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High
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Story freshness
91 days old
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COVERAGE [1]

  1. r/MachineLearning TIER_1 English(EN) · /u/Illustrious_Age_2792 ·

    Best Text to Text Translation Model? [D]

    <!-- SC_OFF --><div class="md"><p>I'm working on a project that translates any language into English.</p> <p>So far, I've tried NMT models like NLLB, MADLAD, and SeamlessM4T v2.</p> <p>The main issue is that they struggle with proper nouns such as:</p> <p>- names</p> <p>- places<…

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