Sequence modeling benchmarks and temporal convolutional networks

Related tags

Text Data & NLPTCN
Overview

Sequence Modeling Benchmarks and Temporal Convolutional Networks (TCN)

This repository contains the experiments done in the work An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling by Shaojie Bai, J. Zico Kolter and Vladlen Koltun.

We specifically target a comprehensive set of tasks that have been repeatedly used to compare the effectiveness of different recurrent networks, and evaluate a simple, generic but powerful (purely) convolutional network on the recurrent nets' home turf.

Experiments are done in PyTorch. If you find this repository helpful, please cite our work:

@article{BaiTCN2018,
	author    = {Shaojie Bai and J. Zico Kolter and Vladlen Koltun},
	title     = {An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling},
	journal   = {arXiv:1803.01271},
	year      = {2018},
}

Domains and Datasets

Update: The code should be directly runnable with PyTorch v1.0.0 or above (PyTorch v>1.3.0 strongly recommended). The older versions of PyTorch are no longer supported.

This repository contains the benchmarks to the following tasks, with details explained in each sub-directory:

  • The Adding Problem with various T (we evaluated on T=200, 400, 600)
  • Copying Memory Task with various T (we evaluated on T=500, 1000, 2000)
  • Sequential MNIST digit classification
  • Permuted Sequential MNIST (based on Seq. MNIST, but more challenging)
  • JSB Chorales polyphonic music
  • Nottingham polyphonic music
  • PennTreebank [SMALL] word-level language modeling (LM)
  • Wikitext-103 [LARGE] word-level LM
  • LAMBADA [LARGE] word-level LM and textual understanding
  • PennTreebank [MEDIUM] char-level LM
  • text8 [LARGE] char-level LM

While some of the large datasets are not included in this repo, we use the observations package to download them, which can be easily installed using pip.

Usage

Each task is contained in its own directory, with the following structure:

[TASK_NAME] /
    data/
    [TASK_NAME]_test.py
    models.py
    utils.py

To run TCN model on the task, one only need to run [TASK_NAME]_test.py (e.g. add_test.py). To tune the hyperparameters, one can specify via argument options, which can been seen via the -h flag.

Owner
CMU Locus Lab
Zico Kolter's Research Group
CMU Locus Lab
Code for paper "Which Training Methods for GANs do actually Converge? (ICML 2018)"

GAN stability This repository contains the experiments in the supplementary material for the paper Which Training Methods for GANs do actually Converg

Lars Mescheder 884 Nov 11, 2022
Repository for fine-tuning Transformers 🤗 based seq2seq speech models in JAX/Flax.

Seq2Seq Speech in JAX A JAX/Flax repository for combining a pre-trained speech encoder model (e.g. Wav2Vec2, HuBERT, WavLM) with a pre-trained text de

Sanchit Gandhi 21 Dec 14, 2022
The simple project to separate mixed voice (2 clean voices) to 2 separate voices.

Speech Separation The simple project to separate mixed voice (2 clean voices) to 2 separate voices. Result Example (Clisk to hear the voices): mix ||

vuthede 31 Oct 30, 2022
Compute distance between sequences. 30+ algorithms, pure python implementation, common interface, optional external libs usage.

TextDistance TextDistance -- python library for comparing distance between two or more sequences by many algorithms. Features: 30+ algorithms Pure pyt

Life4 3k Jan 06, 2023
DAGAN - Dual Attention GANs for Semantic Image Synthesis

Contents Semantic Image Synthesis with DAGAN Installation Dataset Preparation Generating Images Using Pretrained Model Train and Test New Models Evalu

Hao Tang 104 Oct 08, 2022
Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding

⚠️ Checkout develop branch to see what is coming in pyannote.audio 2.0: a much smaller and cleaner codebase Python-first API (the good old pyannote-au

pyannote 2.2k Jan 09, 2023
Simple bots or Simbots is a library designed to create simple bots using the power of python. This library utilises Intent, Entity, Relation and Context model to create bots .

Simple bots or Simbots is a library designed to create simple chat bots using the power of python. This library utilises Intent, Entity, Relation and

14 Dec 15, 2021
Official source for spanish Language Models and resources made @ BSC-TEMU within the "Plan de las Tecnologías del Lenguaje" (Plan-TL).

Spanish Language Models 💃🏻 Corpora 📃 Corpora Number of documents Size (GB) BNE 201,080,084 570GB Models 🤖 RoBERTa-base BNE: https://huggingface.co

PlanTL-SANIDAD 203 Dec 20, 2022
Create a semantic search engine with a neural network (i.e. BERT) whose knowledge base can be updated

Create a semantic search engine with a neural network (i.e. BERT) whose knowledge base can be updated. This engine can later be used for downstream tasks in NLP such as Q&A, summarization, generation

Diego 1 Mar 20, 2022
NeuTex: Neural Texture Mapping for Volumetric Neural Rendering

NeuTex: Neural Texture Mapping for Volumetric Neural Rendering Paper: https://arxiv.org/abs/2103.00762 Running Run on the provided DTU scene cd run ba

Fanbo Xiang 68 Jan 06, 2023
2021搜狐校园文本匹配算法大赛baseline

sohu2021-baseline 2021搜狐校园文本匹配算法大赛baseline 简介 分享了一个搜狐文本匹配的baseline,主要是通过条件LayerNorm来增加模型的多样性,以实现同一模型处理不同类型的数据、形成不同输出的目的。 线下验证集F1约0.74,线上测试集F1约0.73。

苏剑林(Jianlin Su) 45 Sep 06, 2022
Implementation of Natural Language Code Search in the project CodeBERT: A Pre-Trained Model for Programming and Natural Languages.

CodeBERT-Implementation In this repo we have replicated the paper CodeBERT: A Pre-Trained Model for Programming and Natural Languages. We are interest

Tanuj Sur 4 Jul 01, 2022
Pipeline for fast building text classification TF-IDF + LogReg baselines.

Text Classification Baseline Pipeline for fast building text classification TF-IDF + LogReg baselines. Usage Instead of writing custom code for specif

Dani El-Ayyass 57 Dec 07, 2022
🏖 Easy training and deployment of seq2seq models.

Headliner Headliner is a sequence modeling library that eases the training and in particular, the deployment of custom sequence models for both resear

Axel Springer Ideas Engineering GmbH 231 Nov 18, 2022
Espresso: A Fast End-to-End Neural Speech Recognition Toolkit

Espresso Espresso is an open-source, modular, extensible end-to-end neural automatic speech recognition (ASR) toolkit based on the deep learning libra

Yiming Wang 919 Jan 03, 2023
OpenChat: Opensource chatting framework for generative models

OpenChat is opensource chatting framework for generative models.

Hyunwoong Ko 427 Jan 06, 2023
Code for the paper "Flexible Generation of Natural Language Deductions"

Code for the paper "Flexible Generation of Natural Language Deductions"

Kaj Bostrom 12 Nov 11, 2022
Coreference resolution for English, German and Polish, optimised for limited training data and easily extensible for further languages

Coreferee Author: Richard Paul Hudson, msg systems ag 1. Introduction 1.1 The basic idea 1.2 Getting started 1.2.1 English 1.2.2 German 1.2.3 Polish 1

msg systems ag 169 Dec 21, 2022
Code for the paper "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer"

T5: Text-To-Text Transfer Transformer The t5 library serves primarily as code for reproducing the experiments in Exploring the Limits of Transfer Lear

Google Research 4.6k Jan 01, 2023
An open source framework for seq2seq models in PyTorch.

pytorch-seq2seq Documentation This is a framework for sequence-to-sequence (seq2seq) models implemented in PyTorch. The framework has modularized and

International Business Machines 1.4k Jan 02, 2023