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Wmt20 Mlqe Et En

Developed by inseq
This is an Estonian-English neural machine translation model trained using the fairseq toolkit for the WMT20 quality estimation shared task
Downloads 13
Release Time : 1/30/2023

Model Overview

This model is specifically designed for bidirectional translation between Estonian and English, trained on the WMT20 European Parliament parallel corpus to generate high-quality translation results

Model Features

High-quality translation
Trained on WMT20 shared task data, providing accurate translations in specialized domains
Bidirectional translation support
Supports bidirectional translation between Estonian and English
Domain-specific optimization
Trained on the European Parliament parallel corpus, suitable for political and legal domain translations

Model Capabilities

Text translation
Bilingual text generation
Domain-specific translation

Use Cases

Professional translation
Political document translation
Converting European Parliament documents between Estonian and English
Maintaining accuracy of professional terminology and contextual consistency
Legal document translation
Translating legal-related documents and agreements
Ensuring precise conversion of legal terminology
Educational research
Linguistic research
Used for comparative linguistics studies and translation quality evaluation
Providing standardized translation benchmarks
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