MILES
datasets
| MILES | datasets | |
|---|---|---|
| 2 | 15 | |
| 48 | 18,480 | |
| - | 1.2% | |
| 0.0 | 9.5 | |
| about 3 years ago | 4 days ago | |
| Python | Python | |
| - | Apache License 2.0 |
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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.
datasets
- 🐍🐍 23 issues to grow yourself as an exceptional open-source Python expert 🧑💻 🥇
- Mastering ROUGE Matrix: Your Guide to Large Language Model Evaluation for Summarization with Examples
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How to Train Large Models on Many GPUs?
https://github.com/huggingface/datasets
https://github.com/huggingface/transformers
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[D] Can we use Ray for distributed training on vertex ai ? Can someone provide me examples for the same ? Also which dataframe libraries you guys used for training machine learning models on huge datasets (100 gb+) (because pandas can't handle huge data).
https://huggingface.co/docs/datasets backed with an Arrow file or buffer
- Need help with a data science project
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Is there a text evaluation metric that does not need reference text?
I'm looking for an automatic evaluation metric that can score the first text higher (since it's more grammatically correct/better for other reasons). All the metrics for NLG I found require some reference text to match the generated text with, which I don't have.
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FauxPilot – an open-source GitHub Copilot server
And then pass that my_code.json as the dataset name.
[1] https://github.com/huggingface/datasets
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Hugging Face Introduces ‘Datasets’: A Lightweight Community Library For Natural Language Processing (NLP)
Code for https://arxiv.org/abs/2109.02846 found: https://github.com/huggingface/datasets
Quick Read | Paper | Github
- Datasets: A Community Library for Natural Language Processing
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CppCon2020 - Slides and other materials from CppCon 2020
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