Showing 1-20 of 37 projects
Multilingual NLP toolkit for production with PyTorch/TensorFlow
Industrial-strength NLP library for Python with pretrained models and fast processing
Large-scale Chinese natural language processing corpus for training and fine-tuning language models
A comprehensive library of text classification models and techniques built with deep learning.
A comprehensive collection of best practices and examples for natural language processing (NLP) using Python.
A comprehensive search tool for finding Chinese NLP datasets, with support for common English NLP datasets as well.
A text classification model using CNN and RNN implemented with TensorFlow for Chinese text data.
A Python library that provides a simple interface for using popular NLP models like BERT, GPT-2, XLNet, and T5 for various tasks.
Snips NLU is a Python library for extracting meaning from text using natural language processing and machine learning.
Catalyst is an accelerated deep learning R&D library for Python that supports a variety of AI and machine learning use cases.
MTEB is a benchmark for evaluating and comparing text embedding models across multiple tasks and languages.
An extensible NLP framework for building powerful text processing and analysis applications in Python.
A collection of Python tutorials covering a wide range of topics from computer vision to network security.
A library of NLP algorithms and utilities for text classification, generation, extraction, and more using the Transformers library.
Kashgari is a production-level NLP Transfer learning framework built on top of tf.keras for text-labeling and text-classification.
EasyNLP is an easy-to-use NLP toolkit for tasks like text classification, retrieval, and generation.
A comprehensive survey of text classification algorithms and techniques in Python.
A comprehensive set of Keras-based NLP models for text classification, similarity, and more, with support for Chinese and English.
A powerful Python library for advanced text analytics, including classification, clustering, summarization, and sentiment analysis.
Data augmentation for NLP using CNN and RNN, presented at EMNLP 2019
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