How to download a machine learning dataset for PyTorch and TensorFlow in Python

Without any need to download, a variety of popular machine learning datasets can be accessed and streamed with Hub with one line of code. This enables you to explore the datasets and train models without needing to download machine learning datasets regardless of their size. Access classical datasets like CIFAR-10, MNIST or Fashion-MNIST, as well as large datasets like Google Objectron, ImageNet, COCO, and many others in Python.

What is MNIST Dataset?

The MNIST (Modified National Institute of Standards and Technology database) dataset contains a training set of 60,000 images and a test set of 10,000 images of handwritten digits. The handwritten digit images have been size-normalized and centered in a fixed size of 28×28 pixels. The MNIST digits dataset is often used by data scientists who want to try machine learning techniques and pattern recognition methods on real-world data while spending minimal effort on preprocessing and formatting.

What is COCO Dataset?

The COCO (Common Objects in Context) dataset comprises 91 common object categories, 82 of which have more than 5,000 labeled examples. The dataset has 2.5 million labeled instances in 328k photos, created with the help of a large number of crowd workers using unique user interfaces for category detection, instance spotting, and instance segmentation. COCO dataset is a novel dataset with the purpose of improving the state-of-the-art in object detection by aligning the problem within the larger framework of scene understanding.

What is CIFAR 10 Dataset?

The CIFAR10 (Canadian Institute For Advanced Research) dataset consists of 10 classes with 6000 color images of 32×32 resolution for each class. It is divided into 50000 training and 10000 testing images. The test dataset contains exactly 1000 randomly collected images from each class. The training datasets may contain more images per class compared to others but they contain 5000 images per class on average.

What is CIFAR 100 Dataset?

The CIFAR100 (Canadian Institute For Advanced Research) dataset consists of 100 classes with 600 color images of 32×32 resolution for each class. It is divided into 500 training and 100 testing images per class. In CIFAR100, there are 20 superclasses sub-grouped into 100 classes. The dataset comes with two labels for each image such as a “fine” label (class) and a “coarse” label (superclass).

What is the MNIST dataset used for

MNIST is used as a “hello world” example by data scientists worldwide. Typically, MNIST dataset is used as a benchmark dataset, or as a proof-of-concept for training and testing purposes in the field of machine learning.

What is the MNIST dataset for Python?

The MNIST dataset (Modified National Institute of Standards and Technology database) is one of the most popular datasets in machine learning. MNIST is a dataset of 60,000 square 28×28 pixel images of handwritten single digits between 0 and 9. The images are in grayscale format.

How to download the MNIST dataset in Python?

You can load MNIST dataset fast with one line of code using the open-source package Activeloop Hub in Python. See detailed instructions on how to load MNIST dataset training subset or MNIST dataset testing subset in Python.

How to create an Image Dataset like MNIST dataset?

With Activeloop Hub, creating image datasets like the MNIST digits dataset is simple. Simple datasets like MNIST can be created automatically by allowing Hub parse the legacy files into Hub dataset format. More complex datasets can be created manually.

What is the size of each image in the MNIST dataset?

MNIST dataset image size is constant across all images of the dataset. Each MNIST dataset image is a fixed-size 28×28 pixel square image.

MNIST vs Fashion-MNIST. What is the difference between MNIST and Fashion-MNIST?

MNIST and Fashion-MNIST dataset are two separate datasets. However, the Fashion-MNIST dataset is meant to be an MNIST dataset alternative. Fashion-MNIST comprises pictures of clothing items and was published in 2017 by Zalando, a German online retailer. Both datasets are of the same size: 60 000 photos in the training set, as well as 10 000 pictures of clothing in the validation set of the dataset.

What does MNIST stand for?

MNIST is an acronym for the Modified National Institute of Standards and Technology dataset.

Why is MNIST so popular?

Created in 1998, MNIST dataset containing handwritten digits from 0-9 is small in size, simple in structure and easy to use. This makes it a good first dataset to try various pattern recognition methods on, with minimal effort required on data preprocessing..

Is MNIST suitable for deep learning?

Yes, MNIST data set is one of the most used datasets for deep learning. As of July 2022, MNIST only falls behind CIFAR-10, Imagenet, coco datasets in the amount of papers that cite them (or has used as a benchmark dataset.

Sentiment-140 Dataset

Estimated reading: 4 minutes

Sentiment-140 dataset

What is Sentiment-140 Dataset?

Sentiment-140 dataset has 800,000 tweets with positive emoticons, and 800,000 tweets with negative emoticons, for a total of 1,600,000 training tweets as well as a test set of 177 negative tweets and 182 positive tweets with only some data containing emoticons. This dataset is useful for consumers or companies to automatically classify the sentiment of their brands, product, or topic on Twitter as either positive or negative with respect to a query term. The dataset has only tweets in English.

Download Sentiment-140Dataset in Python

Instead of downloading the Sentiment-140 dataset in Python, you can effortlessly load it in Python via our Deep Lake open-source with just one line of code.

Load Sentiment-140 Dataset Training Subset in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/sentiment-140-train")

Load Sentiment-140 Dataset Testing Subset in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/sentiment-140-test")

Sentiment-140 Dataset Structure

Sentiment-140 Data Fields

For the test set:

sentiment_type: tensor containing the polarity of the tweet. 0 represents negative, 2 represents neutral and 4 represents a positive tweet.
tweet_text: the tensor containing the tweeted text.
user: the tensor containing the details of the user who tweeted the text.
id: tensor containing the id of the tweet.
date: tensor containing the date of the tweet.
topic: tensor containing the topic of the tweet.

For the test set:

sentiment_type: tensor containing the polarity of the tweet. 0 represents negative, 2 represents neutral and 4 represents a positive tweet.
tweet_text: the tensor containing the tweeted text.
user: the tensor containing the details of the user who tweeted the text.
query_flag: tensor containing query term, if there is no query, then the value will be NO_QUERY.
date: tensor containing the date of the tweet.
id: tensor containing the id of the tweet.

Sentiment-140 Data Splits

The Sentiment-140 dataset has 800,000 tweets with positive emoticons, and 800,000 tweets with negative emoticons, for a total of 1,600,000 training tweets.
The Sentiment-140 dataset test set was composed of 177 negative tweets and 182 positive tweets with only some data containing emoticons.

How to use Sentiment-140 Dataset with PyTorch and TensorFlow in Python

Train a model on Sentiment-140 dataset with PyTorch in Python

Let’s use Deep Lake built-in PyTorch one-line dataloader to connect the data to the compute:

				
					dataloader = ds.pytorch(num_workers=0, batch_size=4, shuffle=False)

Train a model on Sentiment-140 dataset with TensorFlow in Python

				
					dataloader = ds.tensorflow()

Sentiment-140 Dataset Creation

Data Collection and Normalization Information

The training data was post-processed. Emoticons are removed for training purposes. All tweets containing both positive and negative emotions are filtered out and removed. Retweets or tweets copied from another user have been removed. Tweets containing “:P” are removed. The retweet or repeated tweets are removed from the dataset. The test data was manually collected using web applications.

Additional Information about Sentiment-140 Dataset

Sentiment-140 Dataset Description

Homepage: http://help.sentiment140.com/home
Repository: N/A
Paper: Go, A., Bhayani, R., & Huang, L. (2009). Twitter sentiment classification using distant supervision. CS224N project report, Stanford, 1(12), 2009.
Point of Contact: http://help.sentiment140.com/contact

Sentiment-140 Dataset Curators

Alec Go, Richa Bhayani, and Lei Huang

Sentiment-140 Dataset Licensing Information

Deep Lake users may have access to a variety of publicly available datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have a license to use the datasets. It is your responsibility to determine whether you have permission to use the datasets under their license. If you’re a dataset owner and do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thank you for your contribution to the ML community!

Sentiment-140 Dataset Citation Information

				
					@article{go2009twitter,
  title={Twitter sentiment classification using distant supervision},
  author={Go, Alec and Bhayani, Richa and Huang, Lei},
  journal={CS224N project report, Stanford},
  volume={1},
  number={12},
  pages={2009},
  year={2009}
}