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transformers

huggingface / transformers

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DeBERTa Fast Tokenizer

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brandenchan
brandenchan commented Mar 3, 2021

Hi, I am interested in using the DeBERTa model that was recently implemented here and incorporating it into FARM so that it can also be used in open-domain QA settings through Haystack.

Just wondering why there's only a Slow Tokenizer implemented for DeBERTa and wondering if there are plans to create the Fast Tokeni

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cbaziotis / ekphrasis

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Ekphrasis is a text processing tool, geared towards text from social networks, such as Twitter or Facebook. Ekphrasis performs tokenization, word normalization, word segmentation (for splitting hashtags) and spell correction, using word statistics from 2 big corpora (english Wikipedia, twitter - 330mil english tweets).

nlp tokenizer text-processing semeval nlp-library word-segmentation spelling-correction tokenization text-segmentation spell-corrector word-normalization
  • Updated Feb 8, 2021
  • Python

proycon / pynlpl

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PyNLPl, pronounced as 'pineapple', is a Python library for Natural Language Processing. It contains various modules useful for common, and less common, NLP tasks. PyNLPl can be used for basic tasks such as the extraction of n-grams and frequency lists, and to build simple language model. There are also more complex data types and algorithms. Moreover, there are parsers for file formats common in NLP (e.g. FoLiA/Giza/Moses/ARPA/Timbl/CQL). There are also clients to interface with various NLP specific servers. PyNLPl most notably features a very extensive library for working with FoLiA XML (Format for Linguistic Annotation).

python nlp machine-learning natural-language-processing library linguistics computational-linguistics text-processing nlp-library search-algorithms evaluation-metrics folia language-modelling
  • Updated Mar 13, 2019
  • Python

neomatrix369 / nlp_profiler

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[FEATURE] Automate library release process to GitHub and PyPi

neomatrix369
neomatrix369 commented Oct 25, 2020

Missing functionality

Currently, the release process (to GitHub and PyPi) is done manually, it's prone to errors, and the two scripts used work best in happy-path use-case scenarios while edge-case even though less to worry about are not taken care of, as well as they could have been.

The release to PyPi should be fail-safe as there is no way to revert if a mistake is made.

**Proposed

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2. medium-priority Release enhancement good first issue

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