Xmodel-1.5, a 1B multilingual LLM with a custom unigram tokenizer, outperforms PolyLM-1.7B on several Thai, Arabic, French, and Chinese benchmarks and includes a new Thai evaluation dataset.
scb-mt-en-th-2020: A Large English-Thai Parallel Corpus
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abstract
The primary objective of our work is to build a large-scale English-Thai dataset for machine translation. We construct an English-Thai machine translation dataset with over 1 million segment pairs, curated from various sources, namely news, Wikipedia articles, SMS messages, task-based dialogs, web-crawled data and government documents. Methodology for gathering data, building parallel texts and removing noisy sentence pairs are presented in a reproducible manner. We train machine translation models based on this dataset. Our models' performance are comparable to that of Google Translation API (as of May 2020) for Thai-English and outperform Google when the Open Parallel Corpus (OPUS) is included in the training data for both Thai-English and English-Thai translation. The dataset, pre-trained models, and source code to reproduce our work are available for public use.
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Xmodel-1.5: An 1B-scale Multilingual LLM
Xmodel-1.5, a 1B multilingual LLM with a custom unigram tokenizer, outperforms PolyLM-1.7B on several Thai, Arabic, French, and Chinese benchmarks and includes a new Thai evaluation dataset.