Data pipelines for both TensorFlow and PyTorch!

Related tags

Deep Learningtensorflowkeraspytorchquestion-answeringsequence-classificationdataset-loadersimcsemasked-language-modelstoken-classification
Overview

rapidnlp-datasets

Python package PyPI version Python

Data pipelines for both TensorFlow and PyTorch !

If you want to load public datasets, try:

If you want to load local, personal dataset with minimized boilerplate, use rapidnlp-datasets!

installation

pip install -U rapidnlp-datasets

If you work with PyTorch, you should install PyTorch first.

If you work with TensorFlow, you should install TensorFlow first.

Usage

Here are few examples to show you how to use this library.

sequence-classification-quickstart

In PyTorch,

>>> import torch
>>> from rapidnlp_datasets.pt import DatasetForSequenceClassification
>>> dataset = DatasetForSequenceClassification.from_jsonl_files(
        input_files=["testdata/sequence_classification.jsonl"],
        vocab_file="testdata/vocab.txt",
    )
>>> dataloader = torch.utils.data.DataLoader(dataset, shuffle=True, batch_size=32, collate_fn=dataset.batch_padding_collate)
>>> for idx, batch in enumerate(dataloader):
...     print("No.{} batch: \n{}".format(idx, batch))
...

In TensorFlow,

>>> from rapidnlp_datasets.tf import TFDatasetForSequenceClassifiation
>>> dataset, d = TFDatasetForSequenceClassifiation.from_jsonl_files(
        input_files=["testdata/sequence_classification.jsonl"],
        vocab_file="testdata/vocab.txt",
        return_self=True,
    )
>>> for idx, batch in enumerate(iter(dataset)):
...     print("No.{} batch: \n{}".format(idx, batch))
...

Especially, you can save dataset to tfrecord format when working with TensorFlow, and then build dataset from tfrecord files directly!

>>> d.save_tfrecord("testdata/sequence_classification.tfrecord")
2021-12-08 14:52:41,295    INFO             utils.py  128] Finished to write 2 examples to tfrecords.
>>> dataset = TFDatasetForSequenceClassifiation.from_tfrecord_files("testdata/sequence_classification.tfrecord")
>>> for idx, batch in enumerate(iter(dataset)):
...     print("No.{} batch: \n{}".format(idx, batch))
...

question-answering-quickstart

In PyTorch:

>>> import torch
>>> from rapidnlp_datasets.pt import DatasetForQuestionAnswering
>>>
>>> dataset = DatasetForQuestionAnswering.from_jsonl_files(
        input_files="testdata/qa.jsonl",
        vocab_file="testdata/vocab.txt",
    )
>>> dataloader = torch.utils.data.DataLoader(dataset, shuffle=True, batch_size=32, collate_fn=dataset.batch_padding_collate)
>>> for idx, batch in enumerate(dataloader):
...     print("No.{} batch: \n{}".format(idx, batch))
...

In TensorFlow,

>>> from rapidnlp_datasets.tf import TFDatasetForQuestionAnswering
>>> dataset, d = TFDatasetForQuestionAnswering.from_jsonl_files(
        input_files="testdata/qa.jsonl",
        vocab_file="testdata/vocab.txt",
        return_self=True,
    )
2021-12-08 15:09:06,747    INFO question_answering_dataset.py  101] Read 3 examples in total.
>>> for idx, batch in enumerate(iter(dataset)):
        print()
        print("NO.{} batch: \n{}".format(idx, batch))
...

Especially, you can save dataset to tfrecord format when working with TensorFlow, and then build dataset from tfrecord files directly!

>>> d.save_tfrecord("testdata/qa.tfrecord")
2021-12-08 15:09:31,329    INFO             utils.py  128] Finished to write 3 examples to tfrecords.
>>> dataset = TFDatasetForQuestionAnswering.from_tfrecord_files(
        "testdata/qa.tfrecord",
        batch_size=32,
        padding="batch",
    )
>>> for idx, batch in enumerate(iter(dataset)):
        print()
        print("NO.{} batch: \n{}".format(idx, batch))
...

token-classification-quickstart

masked-language-models-quickstart

simcse-quickstart

You might also like...
In this project we use both Resnet and Self-attention layer for cat, dog and flower classification.
In this project we use both Resnet and Self-attention layer for cat, dog and flower classification.

cdf_att_classification classes = {0: 'cat', 1: 'dog', 2: 'flower'} In this project we use both Resnet and Self-attention layer for cdf-Classification.

null 3 Nov 23, 2022
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

Master status: Development status: Package information: TPOT stands for Tree-based Pipeline Optimization Tool. Consider TPOT your Data Science Assista

Epistasis Lab at UPenn 8.9k Dec 30, 2022
🤗 Push your spaCy pipelines to the Hugging Face Hub
🤗 Push your spaCy pipelines to the Hugging Face Hub

spacy-huggingface-hub: Push your spaCy pipelines to the Hugging Face Hub This package provides a CLI command for uploading any trained spaCy pipeline

Explosion 30 Oct 9, 2022
AI pipelines for Nvidia Jetson Platform

Jetson Multicamera Pipelines Easy-to-use realtime CV/AI pipelines for Nvidia Jetson Platform. This project: Builds a typical multi-camera pipeline, i.

NVIDIA AI IOT 96 Dec 23, 2022
This is a repository for a No-Code object detection inference API using the OpenVINO. It's supported on both Windows and Linux Operating systems.
This is a repository for a No-Code object detection inference API using the OpenVINO. It's supported on both Windows and Linux Operating systems.

OpenVINO Inference API This is a repository for an object detection inference API using the OpenVINO. It's supported on both Windows and Linux Operati

BMW TechOffice MUNICH 68 Nov 24, 2022
Machine learning framework for both deep learning and traditional algorithms
Machine learning framework for both deep learning and traditional algorithms

NeoML is an end-to-end machine learning framework that allows you to build, train, and deploy ML models. This framework is used by ABBYY engineers for

NeoML 704 Dec 27, 2022
CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation

CPT This repository contains code and checkpoints for CPT. CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Gener

fastNLP 341 Dec 29, 2022
A transformer which can randomly augment VOC format dataset (both image and bbox) online.
A transformer which can randomly augment VOC format dataset (both image and bbox) online.

VocAug It is difficult to find a script which can augment VOC-format dataset, especially the bbox. Or find a script needs complex requirements so it i

Coder.AN 1 Mar 5, 2022
Official repository for GCR rerank, a GCN-based reranking method for both image and video re-ID

Official repository for GCR rerank, a GCN-based reranking method for both image and video re-ID

null 53 Nov 22, 2022
Releases(v0.2.0)
Owner
GitHub Repository
Bounding Wasserstein distance with couplings

BoundWasserstein These scripts reproduce the results of the article Bounding Wasserstein distance with couplings by Niloy Biswas and Lester Mackey. ar

Niloy Biswas 1 Jan 11, 2022
OptNet: Differentiable Optimization as a Layer in Neural Networks

OptNet: Differentiable Optimization as a Layer in Neural Networks This repository is by Brandon Amos and J. Zico Kolter and contains the PyTorch sourc

CMU Locus Lab 428 Dec 24, 2022
Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation

OSCAR Project Page | Paper This repository contains the codebase used in OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Ma

NVIDIA Research Projects 74 Dec 22, 2022
Official PyTorch implementation of the preprint paper "Stylized Neural Painting", accepted to CVPR 2021.

Official PyTorch implementation of the preprint paper "Stylized Neural Painting", accepted to CVPR 2021.

Zhengxia Zou 1.5k Dec 28, 2022
LTR_CrossEncoder: Legal Text Retrieval Zalo AI Challenge 2021

LTR_CrossEncoder: Legal Text Retrieval Zalo AI Challenge 2021 We propose a cross encoder model (LTR_CrossEncoder) for information retrieval, re-retrie

Hieu Duong 7 Jan 12, 2022
SuperSDR: multiplatform KiwiSDR + CAT transceiver integrator

SuperSDR SuperSDR integrates a realtime spectrum waterfall and audio receive from any KiwiSDR around the world, together with a local (or remote) cont

Marco Cogoni 30 Nov 29, 2022
A repository for the updated version of CoinRun used to collect MUGEN, a multimodal video-audio-text dataset.

A repository for the updated version of CoinRun used to collect MUGEN, a multimodal video-audio-text dataset. This repo contains scripts to train RL agents to navigate the closed world and collect vi

MUGEN 11 Oct 22, 2022
This repository is to support contributions for tools for the Project CodeNet dataset hosted in DAX

The goal of Project CodeNet is to provide the AI-for-Code research community with a large scale, diverse, and high quality curated dataset to drive innovation in AI techniques.

International Business Machines 1.2k Jan 04, 2023
The implementation of "Bootstrapping Semantic Segmentation with Regional Contrast".

ReCo - Regional Contrast This repository contains the source code of ReCo and baselines from the paper, Bootstrapping Semantic Segmentation with Regio

Shikun Liu 128 Dec 30, 2022
Pytorch implementation of set transformer

set_transformer Official PyTorch implementation of the paper Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks .

Juho Lee 410 Jan 06, 2023
Computer Vision Script to recognize first person motion, developed as final project for the course "Machine Learning and Deep Learning"

Overview of The Code BaseColab/MLDL_FPAR.pdf: it contains the full explanation of our work Base Colab: it contains the base colab used to perform all

Simone Papicchio 4 Jul 16, 2022
Official repository with code and data accompanying the NAACL 2021 paper "Hurdles to Progress in Long-form Question Answering" (https://arxiv.org/abs/2103.06332).

Hurdles to Progress in Long-form Question Answering This repository contains the official scripts and datasets accompanying our NAACL 2021 paper, "Hur

Kalpesh Krishna 41 Nov 08, 2022
U-Net: Convolutional Networks for Biomedical Image Segmentation

Deep Learning Tutorial for Kaggle Ultrasound Nerve Segmentation competition, using Keras This tutorial shows how to use Keras library to build deep ne

Yihui He 401 Nov 21, 2022
Gym environments used in the paper: "Developmental Reinforcement Learning of Control Policy of a Quadcopter UAV with Thrust Vectoring Rotors"

gym_multirotor Gym to train reinforcement learning agents on UAV platforms Quadrotor Tiltrotor Requirements This package has been tested on Ubuntu 18.

Aditya M. Deshpande 19 Dec 29, 2022
Keras implementation of Real-Time Semantic Segmentation on High-Resolution Images

Keras-ICNet [paper] Keras implementation of Real-Time Semantic Segmentation on High-Resolution Images. Training in progress! Requisites Python 3.6.3 K

Aitor Ruano 87 Dec 16, 2022
A Comprehensive Empirical Study of Vision-Language Pre-trained Model for Supervised Cross-Modal Retrieval

CLIP4CMR A Comprehensive Empirical Study of Vision-Language Pre-trained Model for Supervised Cross-Modal Retrieval The original data and pre-calculate

24 Dec 26, 2022
A python library for face detection and features extraction based on mediapipe library

FaceAnalyzer A python library for face detection and features extraction based on mediapipe library Introduction FaceAnalyzer is a library based on me

Saifeddine ALOUI 14 Dec 30, 2022
Classify music genre from a 10 second sound stream using a Neural Network.

MusicGenreClassification Academic research in the field of Deep Learning (Deep Neural Networks) and Sound Processing, Tel Aviv University. Featured in

Matan Lachmish 453 Dec 27, 2022
VolumeGAN - 3D-aware Image Synthesis via Learning Structural and Textural Representations

VolumeGAN - 3D-aware Image Synthesis via Learning Structural and Textural Representations 3D-aware Image Synthesis via Learning Structural and Textura

GenForce: May Generative Force Be with You 116 Dec 26, 2022
OpenLT: An open-source project for long-tail classification

OpenLT: An open-source project for long-tail classification Supported Methods for Long-tailed Recognition: Cross-Entropy Loss Focal Loss (ICCV'17) Cla

Ming Li 37 Sep 15, 2022