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nlp-library

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transformers
patrickvonplaten
patrickvonplaten commented Apr 14, 2021

🚀 Feature request

This is a feature request to add Wav2Vec2 Pretraining functionality to the transformers library. This is a "Good Second Issue" feature request, which means that interested contributors should have some experience with the transformers library and ideally also with training/fine-tuning Wav2Vec2.

Motivation

The popular [Wav2Vec2](https://huggingface.co/models?filter=w

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).

  • Updated Feb 8, 2021
  • Python

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).

  • Updated Mar 13, 2019
  • Python

A python package to run contextualized topic modeling. CTMs combine contextualized embeddings (e.g., BERT) with topic models to get coherent topics. Also supports multilingual tasks. Cross-lingual Zero-shot model published at EACL 2021.

  • Updated Apr 8, 2021
  • Python
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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