MILES
gector
MILES | gector | |
---|---|---|
2 | 2 | |
48 | 864 | |
- | 0.8% | |
0.0 | 0.0 | |
about 3 years ago | 9 months ago | |
Python | Python | |
- | Apache License 2.0 |
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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.
gector
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ML application for grammar correction
If you capture it, you might as well correct it. Check out Gramformer or Grammarly's Gector. You can do scoring based on number of mistakes proposed by these models i.e. the fewer, the better.
- Is there any way to detect grammatical errors and classify text as being either grammatically correct/incorrect?
What are some alternatives?
Gramformer - A framework for detecting, highlighting and correcting grammatical errors on natural language text. Created by Prithiviraj Damodaran. Open to pull requests and other forms of collaboration.
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
spark-nlp - State of the Art Natural Language Processing
DeBERTa - The implementation of DeBERTa
happy-transformer - Happy Transformer makes it easy to fine-tune and perform inference with NLP Transformer models.
adaptnlp - An easy to use Natural Language Processing library and framework for predicting, training, fine-tuning, and serving up state-of-the-art NLP models.
bertviz - BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)
TEAM - Our EMNLP 2022 paper on MCQA