156 Repositories
Python sequence Libraries
Unofficial pytorch implementation for Self-critical Sequence Training for Image Captioning. and others.
An Image Captioning codebase This is a codebase for image captioning research. It supports: Self critical training from Self-critical Sequence Trainin
LightSeq is a high performance training and inference library for sequence processing and generation implemented in CUDA
LightSeq: A High Performance Library for Sequence Processing and Generation
An implementation for `Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction`
Text2Event An implementation for Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction Please contact Yaojie Lu (@
Code for paper "ASAP-Net: Attention and Structure Aware Point Cloud Sequence Segmentation"
ASAP-Net This project implements ASAP-Net of paper ASAP-Net: Attention and Structure Aware Point Cloud Sequence Segmentation (BMVC2020). Overview We i
An official implementation of the paper Exploring Sequence Feature Alignment for Domain Adaptive Detection Transformers
Sequence Feature Alignment (SFA) By Wen Wang, Yang Cao, Jing Zhang, Fengxiang He, Zheng-jun Zha, Yonggang Wen, and Dacheng Tao This repository is an o
MASS: Masked Sequence to Sequence Pre-training for Language Generation
MASS: Masked Sequence to Sequence Pre-training for Language Generation
A PyTorch Implementation of Gated Graph Sequence Neural Networks (GGNN)
A PyTorch Implementation of GGNN This is a PyTorch implementation of the Gated Graph Sequence Neural Networks (GGNN) as described in the paper Gated G
Sequence modeling benchmarks and temporal convolutional networks
Sequence Modeling Benchmarks and Temporal Convolutional Networks (TCN) This repository contains the experiments done in the work An Empirical Evaluati
LogDeep is an open source deeplearning-based log analysis toolkit for automated anomaly detection.
LogDeep is an open source deeplearning-based log analysis toolkit for automated anomaly detection.
Implementation of H-Transformer-1D, Hierarchical Attention for Sequence Learning
H-Transformer-1D Implementation of H-Transformer-1D, Transformer using hierarchical Attention for sequence learning with subquadratic costs. For now,
A Fast Sequence Transducer Implementation with PyTorch Bindings
transducer A Fast Sequence Transducer Implementation with PyTorch Bindings. The corresponding publication is Sequence Transduction with Recurrent Neur
Pervasive Attention: 2D Convolutional Networks for Sequence-to-Sequence Prediction
This is a fork of Fairseq(-py) with implementations of the following models: Pervasive Attention - 2D Convolutional Neural Networks for Sequence-to-Se
[ICLR'19] Trellis Networks for Sequence Modeling
TrellisNet for Sequence Modeling This repository contains the experiments done in paper Trellis Networks for Sequence Modeling by Shaojie Bai, J. Zico
A highly sophisticated sequence-to-sequence model for code generation
CoderX A proof-of-concept AI system by Graham Neubig (June 30, 2021). About CoderX CoderX is a retrieval-based code generation AI system reminiscent o
Code for the RA-L (ICRA) 2021 paper "SeqNet: Learning Descriptors for Sequence-Based Hierarchical Place Recognition"
SeqNet: Learning Descriptors for Sequence-Based Hierarchical Place Recognition [ArXiv+Supplementary] [IEEE Xplore RA-L 2021] [ICRA 2021 YouTube Video]
Sequence model architectures from scratch in PyTorch
This repository implements a variety of sequence model architectures from scratch in PyTorch. Effort has been put to make the code well structured so that it can serve as learning material. The training loop implements the learner design pattern from fast.ai in pure PyTorch, with access to the loop provided through callbacks. Detailed logging and graphs are also provided with python logging and wandb. Additional implementations will be added.
Markup is an online annotation tool that can be used to transform unstructured documents into structured formats for NLP and ML tasks, such as named-entity recognition. Markup learns as you annotate in order to predict and suggest complex annotations. Markup also provides integrated access to existing and custom ontologies, enabling the prediction and suggestion of ontology mappings based on the text you're annotating.
Markup is an online annotation tool that can be used to transform unstructured documents into structured formats for NLP and ML tasks, such as named-entity recognition. Markup learns as you annotate in order to predict and suggest complex annotations. Markup also provides integrated access to existing and custom ontologies, enabling the prediction and suggestion of ontology mappings based on the text you're annotating.
Code for our paper "Transfer Learning for Sequence Generation: from Single-source to Multi-source" in ACL 2021.
TRICE: a task-agnostic transferring framework for multi-source sequence generation This is the source code of our work Transfer Learning for Sequence
Official codebase for Decision Transformer: Reinforcement Learning via Sequence Modeling.
Decision Transformer Lili Chen*, Kevin Lu*, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivasβ , and Igor M
You Only π One Sequence
You Only π One Sequence TL;DR: We study the transferability of the vanilla ViT pre-trained on mid-sized ImageNet-1k to the more challenging COCO obje
Code for the ACL2021 paper "Lexicon Enhanced Chinese Sequence Labelling Using BERT Adapter"
Lexicon Enhanced Chinese Sequence Labeling Using BERT Adapter Code and checkpoints for the ACL2021 paper "Lexicon Enhanced Chinese Sequence Labelling
Simultaneous NMT/MMT framework in PyTorch
This repository includes the codes, the experiment configurations and the scripts to prepare/download data for the Simultaneous Machine Translation wi
CAUSE: Causality from AttribUtions on Sequence of Events
CAUSE: Causality from AttribUtions on Sequence of Events
tsai is an open-source deep learning package built on top of Pytorch & fastai focused on state-of-the-art techniques for time series classification, regression and forecasting.
Time series Timeseries Deep Learning Pytorch fastai - State-of-the-art Deep Learning with Time Series and Sequences in Pytorch / fastai
Sequence-to-Sequence Framework in PyTorch
nmtpytorch allows training of various end-to-end neural architectures including but not limited to neural machine translation, image captioning and au
Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
Fairseq(-py) is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language mod
LightSeq: A High-Performance Inference Library for Sequence Processing and Generation
LightSeq is a high performance inference library for sequence processing and generation implemented in CUDA. It enables highly efficient computation of modern NLP models such as BERT, GPT2, Transformer, etc. It is therefore best useful for Machine Translation, Text Generation, DialogοΌ Language Modelling, and other related tasks using these models.
Task-based datasets, preprocessing, and evaluation for sequence models.
SeqIO: Task-based datasets, preprocessing, and evaluation for sequence models. SeqIO is a library for processing sequential data to be fed into downst
Sequence-to-sequence framework with a focus on Neural Machine Translation based on Apache MXNet
Sequence-to-sequence framework with a focus on Neural Machine Translation based on Apache MXNet
CLASP - Contrastive Language-Aminoacid Sequence Pretraining
CLASP - Contrastive Language-Aminoacid Sequence Pretraining Repository for creating models pretrained on language and aminoacid sequences similar to C
[CVPR 2021] Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers
[CVPR 2021] Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers
An efficient and effective learning to rank algorithm by mining information across ranking candidates. This repository contains the tensorflow implementation of SERank model. The code is developed based on TF-Ranking.
SERank An efficient and effective learning to rank algorithm by mining information across ranking candidates. This repository contains the tensorflow
Understanding and Improving Encoder Layer Fusion in Sequence-to-Sequence Learning (ICLR 2021)
Understanding and Improving Encoder Layer Fusion in Sequence-to-Sequence Learning (ICLR 2021) Citation Please cite as: @inproceedings{liu2020understan
Convolutional Recurrent Neural Networks(CRNN) for Scene Text Recognition
CRNN_Tensorflow This is a TensorFlow implementation of a Deep Neural Network for scene text recognition. It is mainly based on the paper "An End-to-En
a Deep Learning Framework for Text
DeLFT DeLFT (Deep Learning Framework for Text) is a Keras and TensorFlow framework for text processing, focusing on sequence labelling (e.g. named ent
Open Sound Strip, Sequence or Record in Audacity
Audacity Tools For Blender Sound editing in Blender Video Sequence Editor with Audacity integrated. Send/receive the full edited sequence or single st
A very simple framework for state-of-the-art Natural Language Processing (NLP)
A very simple framework for state-of-the-art NLP. Developed by Humboldt University of Berlin and friends. Flair is: A powerful NLP library. Flair allo
Sequence learning toolkit for Python
seqlearn seqlearn is a sequence classification toolkit for Python. It is designed to extend scikit-learn and offer as similar as possible an API. Comp
Implementation of SETR model, Original paper: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers.
SETR - Pytorch Since the original paper (Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers.) has no official
Bidirectional LSTM-CRF and ELMo for Named-Entity Recognition, Part-of-Speech Tagging and so on.
anaGo anaGo is a Python library for sequence labeling(NER, PoS Tagging,...), implemented in Keras. anaGo can solve sequence labeling tasks such as nam
Kashgari is a production-level NLP Transfer learning framework built on top of tf.keras for text-labeling and text-classification, includes Word2Vec, BERT, and GPT2 Language Embedding.
Kashgari Overview | Performance | Installation | Documentation | Contributing π π π We released the 2.0.0 version with TF2 Support. π π π If you
Sequence-to-sequence framework with a focus on Neural Machine Translation based on Apache MXNet
Sockeye This package contains the Sockeye project, an open-source sequence-to-sequence framework for Neural Machine Translation based on Apache MXNet
DELTA is a deep learning based natural language and speech processing platform.
DELTA - A DEep learning Language Technology plAtform What is DELTA? DELTA is a deep learning based end-to-end natural language and speech processing p
A very simple framework for state-of-the-art Natural Language Processing (NLP)
A very simple framework for state-of-the-art NLP. Developed by Humboldt University of Berlin and friends. IMPORTANT: (30.08.2020) We moved our models
Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
Fairseq(-py) is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language mod
Bidirectional LSTM-CRF and ELMo for Named-Entity Recognition, Part-of-Speech Tagging and so on.
anaGo anaGo is a Python library for sequence labeling(NER, PoS Tagging,...), implemented in Keras. anaGo can solve sequence labeling tasks such as nam
Kashgari is a production-level NLP Transfer learning framework built on top of tf.keras for text-labeling and text-classification, includes Word2Vec, BERT, and GPT2 Language Embedding.
Kashgari Overview | Performance | Installation | Documentation | Contributing π π π We released the 2.0.0 version with TF2 Support. π π π If you
Sequence-to-sequence framework with a focus on Neural Machine Translation based on Apache MXNet
Sockeye This package contains the Sockeye project, an open-source sequence-to-sequence framework for Neural Machine Translation based on Apache MXNet
DELTA is a deep learning based natural language and speech processing platform.
DELTA - A DEep learning Language Technology plAtform What is DELTA? DELTA is a deep learning based end-to-end natural language and speech processing p
A very simple framework for state-of-the-art Natural Language Processing (NLP)
A very simple framework for state-of-the-art NLP. Developed by Humboldt University of Berlin and friends. IMPORTANT: (30.08.2020) We moved our models
Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
Fairseq(-py) is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language mod
Array is a functional mutable sequence inheriting from Python's built-in list.
funct.Array Array is a functional mutable sequence inheriting from Python's built-in list. Array provides 100+ higher-order methods and more functiona
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting This is the origin Pytorch implementation of Informer in the followin
Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers
Segmentation Transformer Implementation of Segmentation Transformer in PyTorch, a new model to achieve SOTA in semantic segmentation while using trans
CrossNER: Evaluating Cross-Domain Named Entity Recognition (AAAI-2021)
CrossNER is a fully-labeled collected of named entity recognition (NER) data spanning over five diverse domains (Politics, Natural Science, Music, Literature, and Artificial Intelligence) with specialized entity categories for different domains.
Yet Another Sequence Encoder - Encode sequences to vector of vector in python !
Yase Yet Another Sequence Encoder - encode sequences to vector of vectors in python ! Why Yase ? Yase enable you to encode any sequence which can be r