MILES
best-of-ml-python
| MILES | best-of-ml-python | |
|---|---|---|
| 2 | 16 | |
| 48 | 15,633 | |
| - | 2.6% | |
| 0.0 | 7.8 | |
| about 3 years ago | 5 days ago | |
| Python | Python | |
| - | Creative Commons Attribution Share Alike 4.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
MILES
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MILES — A language-agnostic text simplifier using multilingual BERT
MILES is a multilingual text simplifier inspired by LSBert — A BERT-based lexical simplification approach proposed in 2018. Unlike LSBert, MILES uses the bert-base-multilingual-uncased model, as well as simple language-agnostic approaches to complex word identification (CWI) and candidate ranking. Although not all have been tested, MILES should support 22 languages: Arabic, Bulgarian, Catalan, Czech, Danish, Dutch, English, Finnish, French, German, Hungarian, Indonesian, Italian, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swedish, Turkish, and Ukrainian.
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[P] Meeting MILES - My simple lexical text simplifier using Multilingual BERT
Recently, I started working on another simplifier called MILES. MILES is loosely inspired by LSBert — another lexical simplifier that uses the large BERT uncased model to find substitutions for complex words. MILES works in a very similar way, however, it instead makes use of the multilingual BERT model, as well as fully language-agnostic methods for complex word identification and substitution ranking. As a result, MILES can (in theory) support a multitude of different languages. The GitHub repository can be found here, and below I've included an example text simplified by MILES, as well as an overview of the framework.
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