Deepvoice3_pytorch PyTorch implementation of convolutional networks-based text-to-speech synthesis models: arXiv:1710.07654: Deep Voice 3: Scaling Tex
Segmenter - Transformer for Semantic Segmentation
Russian GPT-3 models (ruGPT3XL, ruGPT3Large, ruGPT3Medium, ruGPT3Small) trained with 2048 sequence length with sparse and dense attention blocks. We also provide Russian GPT-2 large model (ruGPT2Large) trained with 1024 sequence length.
Code for the paper: Sequence-to-Sequence Learning with Latent Neural Grammars
douban_embedding 豆瓣中文影评差评分析 1. NLP NLP（Natural Language Processing）是指自然语言处理，他的目的是让计算机可以听懂人话。 下面是我将2万条豆瓣影评训练之后，随意输入一段新影评交给神经网络，最终AI推断出的结果。 "很好，演技不错
TextAttack 🐙 Generating adversarial examples for NLP models [TextAttack Documentation on ReadTheDocs] About • Setup • Usage • Design About TextAttack
Compact Transformers Preprint Link: Escaping the Big Data Paradigm with Compact Transformers By Ali Hassani*, Steven Walton*, Nikhil Shah, Ab
A Statutory Article Retrieval Dataset in French This repository contains the Belgian Statutory Article Retrieval Dataset (BSARD), as well as the code
CRNN paper：An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition 1. create your ow
A Python multilingual toolkit for Sentiment Analysis and Social NLP tasks
Fairseq(-py) is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language mod
OpenNMT-py: Open-Source Neural Machine Translation OpenNMT-py is the PyTorch version of the OpenNMT project, an open-source (MIT) neural machine trans
Text preprocessing, representation and visualization from zero to hero. From zero to hero • Installation • Getting Started • Examples • API • FAQ • Co
Silero Models: pre-trained speech-to-text, text-to-speech models and benchmarks made embarrassingly simple
Reranker is a lightweight, effective and efficient package for training and deploying deep languge model reranker in information retrieval (IR), question answering (QA) and many other natural language processing (NLP) pipelines. The training procedure follows our ECIR paper Rethink Training of BERT Rerankers in Multi-Stage Retrieval Pipeline using a localized constrastive esimation (LCE) loss.
Translations 🇩🇪 DE 🇫🇷 FR 🇭🇺 HU 🇮🇩 ID 🇮🇹 IT 🇳🇱 NL 🇧🇷 PT-BR 🇷🇺 RU 🇨🇳 ZH ➡️ Documentation | Discord | Installation Guide ⬅️ Fully autom
FuzzyWuzzy Fuzzy string matching like a boss. It uses Levenshtein Distance to calculate the differences between sequences in a simple-to-use package.
AEC_DeepModel - Deep learning based acoustic echo cancellation baseline code
GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields Project Page | Paper | Supplementary | Video | Slides | Blog | Talk If
This is the code of "Scene Text Retrieval via Joint Text Detection and Similarity Learning". For more details, please refer to our CVPR2021 paper.
OpenNRE We have a DEMO website (http://opennre.thunlp.ai/). Try it out! OpenNRE is an open-source and extensible toolkit that provides a unified frame
NVIDIA NeMo Introduction NeMo is a toolkit for creating Conversational AI applications. NeMo product page. Introductory video. The toolkit comes with
textacy: NLP, before and after spaCy textacy is a Python library for performing a variety of natural language processing (NLP) tasks, built on the hig
SUBSHOP Tools to download, remove ads, and synchronize subtitles. SUBSHOP Purpose Limitations Required Web Credentials Installation, Configuration, an
TextDistance TextDistance -- python library for comparing distance between two or more sequences by many algorithms. Features: 30+ algorithms Pure pyt
lambeq About lambeq is a toolkit for quantum natural language processing (QNLP). Documentation: https://cqcl.github.io/lambeq/ Getting started Prerequ
The implementation of paper CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval. CLIP4Clip is a video-text retrieval model based
TensorFlow Text - Text processing in Tensorflow IMPORTANT: When installing TF Text with pip install, please note the version of TensorFlow you are run