Around 500K essays are available in this dataset, both created by AI and written by Human.

I have gathered the data from multiple sources, added them together and removed the duplicates

These data consist of a collection of legitimate as well as phishing website instances. Each website is represented by the set of features which denote, whether website is legitimate or not. Data can serve as an input for machine learning process.

Here, the two variants of the Phishing Dataset are presented.

1. Full variant - dataset_full.csv

   • Total number of instances: 88,647
   • Number of legitimate website instances (labeled as 0): 58,000
   • Number of phishing website instances (labeled as 1): 30,647
   • Total number of features: 111

2. Small variant - dataset_small.csv

   • Total number of instances: 58,645
   • Number of legitimate website instances (labeled as 0): 27,998
   • Number of phishing website instances (labeled as 1): 30,647
   • Total number of features: 111

Analysis of ‘Popular Website Traffic Over Time ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/popular-website-traffice on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

Background

Have you every been in a conversation and the question comes up, who uses Bing? This question comes up occasionally because people wonder if these sites have any views. For this research study, we are going to be exploring popular website traffic for many popular websites.

Methodology

The data collected originates from SimilarWeb.com.

Source

For the analysis and study, go to The Concept Center

This dataset was created by Chase Willden and contains around 0 samples along with 1/1/2017, Social Media, technical information and other features such as: - 12/1/2016 - 3/1/2017 - and more.

How to use this dataset

• Analyze 11/1/2016 in relation to 2/1/2017
• Study the influence of 4/1/2017 on 1/1/2017
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Chase Willden

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Analysis of ‘Kaggle Datasets Ranking’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/vivovinco/kaggle-datasets-ranking on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

This dataset contains Kaggle ranking of datasets.

Content

+800 rows and 8 columns. Columns' description are listed below.

• Rank : Rank of the user
• Tier : Grandmaster, Master or Expert
• Username : Name of the user
• Join Date : Year of join
• Gold Medals : Number of gold medals
• Silver Medals : Number of silver medals
• Bronze Medals : Number of bronze medals
• Points : Total points

Acknowledgements

Data from Kaggle. Image from The Guardian.

If you're reading this, please upvote.

--- Original source retains full ownership of the source dataset ---

The Community-Driven Model Service Platform market is experiencing robust growth, projected to reach $35.14 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 10.1% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing availability of open-source machine learning models and datasets, facilitated by platforms like Kaggle, GitHub, and Hugging Face, significantly lowers the barrier to entry for developers and researchers. Furthermore, the growing demand for specialized, niche models that cater to specific industry needs is propelling market growth. The rise of cloud-based solutions offers scalability and cost-effectiveness, attracting a wider user base. The market segmentation reveals strong growth in both adult and children's applications, reflecting the broadening use cases for these platforms across various age demographics. Cloud-based platforms dominate the market share due to their flexibility and accessibility, though on-premise solutions retain a significant presence in enterprises prioritizing data security and control. Geographic distribution showcases a strong presence in North America and Europe, driven by established tech ecosystems and high adoption rates of AI technologies. However, rapid growth is anticipated in the Asia-Pacific region, particularly in countries like China and India, due to increasing digitalization and investment in AI infrastructure. The market's restraints are primarily focused on the need for robust data governance and ethical considerations surrounding AI model development and deployment. Concerns about data bias, model explainability, and potential misuse of AI models need to be addressed to ensure responsible growth. Furthermore, the skill gap in AI development and deployment presents a challenge, requiring substantial investment in education and training initiatives to support the expanding market. Competition among platform providers is intense, necessitating continuous innovation and the development of unique value propositions to maintain market share. Despite these challenges, the long-term outlook for the Community-Driven Model Service Platform market remains exceptionally positive, driven by sustained technological advancements and the increasing reliance on AI across diverse industries. The platform's potential to foster collaboration and democratize access to powerful machine learning tools is poised to drive considerable market expansion in the coming years.

In recent years, social Q&A sites have developed rapidly, like Zhihu and Quora, with hundreds of millions of users, providing a convenient platform for users to ask questions and share knowledge. Most users share knowledge through real-name answers, but this sometimes hinders knowledge sharing, such as employees sharing company salaries and students sharing inside information about the laboratory. Revealing the author's true identity, in this case, can cause significant harm to the author. In order to tackle the above problems, social question-and-answer websites provide users with an anonymous answer function, which replaces the real author id of the answer with an anonymous id. Countless users who cannot disclose their identities use anonymous answers to share valuable knowledge.

Although there are countless anonymous answers in the Q&A community, few anonymization techniques have been used. The two super-large Q&A sites, Quora and Zhihu, use two anonymization technologies are hiding the author's information and protecting the storage of anonymous user information. However, anonymous answers, questions, comments, and topics and their topological structure contain distinct attributes unknown to most users, providing valuable features for de-anonymization attacks. Although the question-and-answer website warns anonymous users that personal specific information and language styles in answers may lead to privacy leaks, the question-and-answer community cannot give the probability and cause of privacy leaks for a specific anonymous answer.

In this paper, we propose a novel task, the de-anonymization of the Q&A websites, which refers to recovering the identity information of the real author of the anonymous answer. This task aims to evaluate the risk of privacy leakage of a specific anonymous answer in the question-and-answer websites and explain why the answer is vulnerable to de-anonymization.

To explore the effectiveness of various methodologies, we employ web scraping techniques on public answers from online platforms Zhihu and Quora. The first step involves the selection of seed users related to the ten popular topics. We selected one seed user from each topic. This step can ensure that the collected Q&A community dataset encompasses a diverse range of popular topics. In the second step, we recursively crawl the social relationships between users based on the ten seed users crawled in the first step. In order to make the crawled user pool more widely distributed, we only crawled the first 100 following users for each user until the crawled user pool reached 700,000. Step three is to ascertain the users to be ultimately crawled. A community discovery algorithm is employed to identify a community with the highest transitivity. This community must have a population exceeding 5,000. As a result, we will crawl all users in this community. The fourth step involves extracting the data related to all users within the chosen community. To mitigate the issue of excessive data volume, this article sets a constraint on the number of answers collected by individual users during the crawling process. This upper limit ensures that all answers of 95% of users are crawled. The crawled data consists of user homepage information, relationships between users, user-generated questions and answers, and associated comments and topics. Each question includes its title and the name of the person who asked it. Each answer contains the author's name, time of submission, the content of the answer, and any first-level comments. Additionally, each comment includes its author, submission time, and content.

Here is an example code to read the dataset https://www.kaggle.com/tianbaojie/example-code.

Analysis of ‘CENSORED WEB-SITES BY ALL COUNTRIES’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/brsdincer/censored-websites-by-all-countries on 28 January 2022.

--- Dataset description provided by original source is as follows ---

CENSORED WEB-SITES BY ALL COUNTRIES

Sites that were or are currently banned.

This data was created by each country's own users.

• Some of the sites you have seen may have been active again.

--- Original source retains full ownership of the source dataset ---

The BDG2 open data set consists of 3,053 energy meters from 1,636 non-residential buildings with a range of two full years (2016 and 2017) at an hourly frequency (17,544 measurements per meter resulting in approximately 53.6 million measurements). These meters are collected from 19 sites across North America and Europe, and they measure electrical, heating and cooling water, steam, and solar energy as well as water and irrigation meters. Part of these data was used in the Great Energy Predictor III (GEPIII) competition hosted by the ASHRAE organization in October-December 2019. This subset includes data from 2,380 meters from 1,448 buildings that were used in the GEPIII, a machine learning competition for long-term prediction with an application to measurement and verification. This paper describes the process of data collection, cleaning, and convergence of time-series meter data, the meta-data about the buildings, and complementary weather data. This data set can be used for further prediction benchmarking and prototyping as well as anomaly detection, energy analysis, and building type classification.

This is a dataset containing 10,000 posts from Kaggle and 60,000 comments related to those posts in the question-answer topic.

  Data Fields







  kaggle_post

'pseudo', The question authors. 'title', Title of the Post. 'question', The question's body. 'vote', Voting on Kaggle is similar to liking. 'medal', I will share with you the Kaggle medal system, which can be found at https://www.kaggle.com/progression. The system awards medals to users based on… See the full description on the dataset page: https://huggingface.co/datasets/Raaxx/Kaggle-post-and-comments-question-answer-topic.

Personality Dataset

Essays https://huggingface.co/datasets/jingjietan/essays-big5 MBTI https://huggingface.co/datasets/jingjietan/kaggle-mbti Pandora https://huggingface.co/datasets/jingjietan/pandora-big5 Please contact jingjietan.com for another dataset. Cite: @software{jingjietan-apr-dataset, author = {Jing Jie, Tan}, title = {Personality Kaggle Dataset Splitting}, url = {https://huggingface.co/datasets/jingjietan/kaggle-mbti}, version = {1.0.0}, year = {2024} }

FSDKaggle2018 is an audio dataset containing 11,073 audio files annotated with 41 labels of the AudioSet Ontology. FSDKaggle2018 has been used for the DCASE Challenge 2018 Task 2, which was run as a Kaggle competition titled Freesound General-Purpose Audio Tagging Challenge.

Citation

If you use the FSDKaggle2018 dataset or part of it, please cite our DCASE 2018 paper:

Eduardo Fonseca, Manoj Plakal, Frederic Font, Daniel P. W. Ellis, Xavier Favory, Jordi Pons, Xavier Serra. "General-purpose Tagging of Freesound Audio with AudioSet Labels: Task Description, Dataset, and Baseline". Proceedings of the DCASE 2018 Workshop (2018)

You can also consider citing our ISMIR 2017 paper, which describes how we gathered the manual annotations included in FSDKaggle2018.

Eduardo Fonseca, Jordi Pons, Xavier Favory, Frederic Font, Dmitry Bogdanov, Andres Ferraro, Sergio Oramas, Alastair Porter, and Xavier Serra, "Freesound Datasets: A Platform for the Creation of Open Audio Datasets", In Proceedings of the 18th International Society for Music Information Retrieval Conference, Suzhou, China, 2017

Contact

You are welcome to contact Eduardo Fonseca should you have any questions at eduardo.fonseca@upf.edu.

About this dataset

Freesound Dataset Kaggle 2018 (or FSDKaggle2018 for short) is an audio dataset containing 11,073 audio files annotated with 41 labels of the AudioSet Ontology [1]. FSDKaggle2018 has been used for the Task 2 of the Detection and Classification of Acoustic Scenes and Events (DCASE) Challenge 2018. Please visit the DCASE2018 Challenge Task 2 website for more information. This Task was hosted on the Kaggle platform as a competition titled Freesound General-Purpose Audio Tagging Challenge. It was organized by researchers from the Music Technology Group of Universitat Pompeu Fabra, and from Google Research’s Machine Perception Team.

The goal of this competition was to build an audio tagging system that can categorize an audio clip as belonging to one of a set of 41 diverse categories drawn from the AudioSet Ontology.

All audio samples in this dataset are gathered from Freesound [2] and are provided here as uncompressed PCM 16 bit, 44.1 kHz, mono audio files. Note that because Freesound content is collaboratively contributed, recording quality and techniques can vary widely.

The ground truth data provided in this dataset has been obtained after a data labeling process which is described below in the Data labeling process section. FSDKaggle2018 clips are unequally distributed in the following 41 categories of the AudioSet Ontology:

"Acoustic_guitar", "Applause", "Bark", "Bass_drum", "Burping_or_eructation", "Bus", "Cello", "Chime", "Clarinet", "Computer_keyboard", "Cough", "Cowbell", "Double_bass", "Drawer_open_or_close", "Electric_piano", "Fart", "Finger_snapping", "Fireworks", "Flute", "Glockenspiel", "Gong", "Gunshot_or_gunfire", "Harmonica", "Hi-hat", "Keys_jangling", "Knock", "Laughter", "Meow", "Microwave_oven", "Oboe", "Saxophone", "Scissors", "Shatter", "Snare_drum", "Squeak", "Tambourine", "Tearing", "Telephone", "Trumpet", "Violin_or_fiddle", "Writing".

Some other relevant characteristics of FSDKaggle2018:

• The dataset is split into a train set and a test set.

• The train set is meant to be for system development and includes ~9.5k samples unequally distributed among 41 categories. The minimum number of audio samples per category in the train set is 94, and the maximum 300. The duration of the audio samples ranges from 300ms to 30s due to the diversity of the sound categories and the preferences of Freesound users when recording sounds. The total duration of the train set is roughly 18h.

• Out of the ~9.5k samples from the train set, ~3.7k have manually-verified ground truth annotations and ~5.8k have non-verified annotations. The non-verified annotations of the train set have a quality estimate of at least 65-70% in each category. Checkout the Data labeling process section below for more information about this aspect.

• Non-verified annotations in the train set are properly flagged in train.csv so that participants can opt to use this information during the development of their systems.

• The test set is composed of 1.6k samples with manually-verified annotations and with a similar category distribution than that of the train set. The total duration of the test set is roughly 2h.

• All audio samples in this dataset have a single label (i.e. are only annotated with one label). Checkout the Data labeling process section below for more information about this aspect. A single label should be predicted for each file in the test set.

Data labeling process

The data labeling process started from a manual mapping between Freesound tags and AudioSet Ontology categories (or labels), which was carried out by researchers at the Music Technology Group, Universitat Pompeu Fabra, Barcelona. Using this mapping, a number of Freesound audio samples were automatically annotated with labels from the AudioSet Ontology. These annotations can be understood as weak labels since they express the presence of a sound category in an audio sample.

Then, a data validation process was carried out in which a number of participants did listen to the annotated sounds and manually assessed the presence/absence of an automatically assigned sound category, according to the AudioSet category description.

Audio samples in FSDKaggle2018 are only annotated with a single ground truth label (see train.csv). A total of 3,710 annotations included in the train set of FSDKaggle2018 are annotations that have been manually validated as present and predominant (some with inter-annotator agreement but not all of them). This means that in most cases there is no additional acoustic material other than the labeled category. In few cases there may be some additional sound events, but these additional events won't belong to any of the 41 categories of FSDKaggle2018.

The rest of the annotations have not been manually validated and therefore some of them could be inaccurate. Nonetheless, we have estimated that at least 65-70% of the non-verified annotations per category in the train set are indeed correct. It can happen that some of these non-verified audio samples present several sound sources even though only one label is provided as ground truth. These additional sources are typically out of the set of the 41 categories, but in a few cases they could be within.

More details about the data labeling process can be found in [3].

License

FSDKaggle2018 has licenses at two different levels, as explained next.

All sounds in Freesound are released under Creative Commons (CC) licenses, and each audio clip has its own license as defined by the audio clip uploader in Freesound. For attribution purposes and to facilitate attribution of these files to third parties, we include a relation of the audio clips included in FSDKaggle2018 and their corresponding license. The licenses are specified in the files train_post_competition.csv and test_post_competition_scoring_clips.csv.

In addition, FSDKaggle2018 as a whole is the result of a curation process and it has an additional license. FSDKaggle2018 is released under CC-BY. This license is specified in the LICENSE-DATASET file downloaded with the FSDKaggle2018.doc zip file.

Files

FSDKaggle2018 can be downloaded as a series of zip files with the following directory structure:

root
│

└───FSDKaggle2018.audio_train/            Audio clips in the train set
│

└───FSDKaggle2018.audio_test/            Audio clips in the test set
│

└───FSDKaggle2018.meta/               Files for evaluation setup
│  │

│  └───train_post_competition.csv          Data split and ground truth for the train set
│  │

│  └───test_post_competition_scoring_clips.csv   Ground truth for the test set

│

└───FSDKaggle2018.doc/
  │

  └───README.md                  The dataset description file you are reading
  │

  └───LICENSE-DATASET             

About sites

All the sites were obtained from different sources.

Context

There are lots of datasets available for different machine learning tasks like NLP, Computer vision etc. However I couldn't find any dataset which catered to the domain of software testing. This is one area which has lots of potential for application of Machine Learning techniques specially deep-learning.

This was the reason I wanted such a dataset to exist. So, I made one.

Content

New version [28th Nov'20]- Uploaded testing related questions and related details from stack-overflow. These are query results which were collected from stack-overflow by using stack-overflow's query viewer. The result set of this query contained posts which had the words "testing web pages".

New version[27th Nov'20] - Created a csv file containing pairs of test case titles and test case description.

This dataset is very tiny (approximately 200 rows of data). I have collected sample test cases from around the web and created a text file which contains all the test cases that I have collected. This text file has sections and under each section there are numbered rows of test cases.

Acknowledgements

I would like to thank websites like guru99.com, softwaretestinghelp.com and many other such websites which host great many sample test cases. These were the source for the test cases in this dataset.

Inspiration

My Inspiration to create this dataset was the scarcity of examples showcasing the implementation of machine learning on the domain of software testing. I would like to see if this dataset can be used to answer questions similar to the following--> * Finding semantic similarity between different test cases ranging across products and applications. * Automating the elimination of duplicate test cases in a test case repository. * Cana recommendation system be built for suggesting domain specific test cases to software testers.

This dataset is an extension of my previous work on creating a dataset for natural language processing tasks. It leverages binary representation to characterise various machine learning models. The attributes in the dataset are derived from a dictionary, which was constructed from a corpus of prompts typically provided to a large language model (LLM). These prompts reference specific machine learning algorithms and their implementations. For instance, consider a user asking an LLM or a generative AI to create a Multi-Layer Perceptron (MLP) model for a particular application. By applying this concept to multiple machine learning models, we constructed our corpus. This corpus was then transformed into the current dataset using a bag-of-words approach. In this dataset, each attribute corresponds to a word from our dictionary, represented as a binary value: 1 indicates the presence of the word in a given prompt, and 0 indicates its absence. At the end of each entry, there is a label. Each entry in the dataset pertains to a single class, where each class represents a distinct machine learning model or algorithm. This dataset is intended for multi-class classification tasks, not multi-label classification, as each entry is associated with only one label and does not belong to multiple labels simultaneously. This dataset has been utilised with a Convolutional Neural Network (CNN) using the Keras Automodel API, achieving impressive training and testing accuracy rates exceeding 97%. Post-training, the model's predictive performance was rigorously evaluated in a production environment, where it continued to demonstrate exceptional accuracy. For this evaluation, we employed a series of questions, which are listed below. These questions were intentionally designed to be similar to ensure that the model can effectively distinguish between different machine learning models, even when the prompts are closely related.

KNN How would you create a KNN model to classify emails as spam or not spam based on their content and metadata? How could you implement a KNN model to classify handwritten digits using the MNIST dataset? How would you use a KNN approach to build a recommendation system for suggesting movies to users based on their ratings and preferences? How could you employ a KNN algorithm to predict the price of a house based on features such as its location, size, and number of bedrooms etc? Can you create a KNN model for classifying different species of flowers based on their petal length, petal width, sepal length, and sepal width? How would you utilise a KNN model to predict the sentiment (positive, negative, or neutral) of text reviews or comments? Can you create a KNN model for me that could be used in malware classification? Can you make me a KNN model that can detect a network intrusion when looking at encrypted network traffic? Can you make a KNN model that would predict the stock price of a given stock for the next week? Can you create a KNN model that could be used to detect malware when using a dataset relating to certain permissions a piece of software may have access to?

Decision Tree Can you describe the steps involved in building a decision tree model to classify medical images as malignant or benign for cancer diagnosis and return a model for me? How can you utilise a decision tree approach to develop a model for classifying news articles into different categories (e.g., politics, sports, entertainment) based on their textual content? What approach would you take to create a decision tree model for recommending personalised university courses to students based on their academic strengths and weaknesses? Can you describe how to create a decision tree model for identifying potential fraud in financial transactions based on transaction history, user behaviour, and other relevant data? In what ways might you apply a decision tree model to classify customer complaints into different categories determining the severity of language used? Can you create a decision tree classifier for me? Can you make me a decision tree model that will help me determine the best course of action across a given set of strategies? Can you create a decision tree model for me that can recommend certain cars to customers based on their preferences and budget? How can you make a decision tree model that will predict the movement of star constellations in the sky based on data provided by the NASA website? How do I create a decision tree for time-series forecasting?

Random Forest Can you describe the steps involved in building a random forest model to classify different types of anomalies in network traffic data for cybersecurity purposes and return the code for me? In what ways could you implement a random forest model to predict the severity of traffic congestion in urban areas based on historical traffic patterns, weather...

Context

The MNIST dataset is one of the best known image classification problems out there, and a veritable classic of the field of machine learning. This dataset is more challenging version of the same root problem: classifying letters from images. This is a multiclass classification dataset of glyphs of English letters A - J.

This dataset is used extensively in the Udacity Deep Learning course, and is available in the Tensorflow Github repo (under Examples). I'm not aware of any license governing the use of this data, so I'm posting it here so that the community can use it with Kaggle kernels.

Content

notMNIST _large.zip is a large but dirty version of the dataset with 529,119 images, and notMNIST_small.zip is a small hand-cleaned version of the dataset, with 18726 images. The dataset was assembled by Yaroslav Bulatov, and can be obtained on his blog. According to this blog entry there is about a 6.5% label error rate on the large uncleaned dataset, and a 0.5% label error rate on the small hand-cleaned dataset.

The two files each containing 28x28 grayscale images of letters A - J, organized into directories by letter. notMNIST_large.zip contains 529,119 images and notMNIST_small.zip contains 18726 images.

Acknowledgements

Thanks to Yaroslav Bulatov for putting together the dataset.

• Version 4: Adding the data from "LLM-generated essay using PaLM from Google Gen-AI" kindly generated by Kingki19 / Muhammad Rizqi.
  File: train_essays_RDizzl3_seven_v2.csv
  Human texts: 14247 LLM texts: 3004
  
  See also: a new dataset of an additional 4900 LLM generated texts: LLM: Mistral-7B Instruct texts

  
  
  

• Version 3: "**The RDizzl3 Seven**"
  File: train_essays_RDizzl3_seven_v1.csv

• "Car-free cities"

• "Does the electoral college work?"

• "Exploring Venus"

• "The Face on Mars"

• "Facial action coding system"

• "A Cowboy Who Rode the Waves"

• "Driverless cars"

How this dataset was made: see the notebook "LLM: Make 7 prompt train dataset"

• Version 2: (train_essays_7_prompts_v2.csv) This dataset is composed of 13,712 human texts and 1638 AI-LLM generated texts originating from 7 of the PERSUADE 2.0 corpus prompts.

Namely:

• "Car-free cities"
• "Does the electoral college work?"
• "Exploring Venus"
• "The Face on Mars"
• "Facial action coding system"
• "Seeking multiple opinions"
• "Phones and driving"

This dataset is a derivative of the datasets

• LLM Generated Essays for the Detect AI Comp! by Radek Osmulski
• persuade corpus 2.0 provided by Nicholas Broad
• daigt data - llama 70b and falcon180b by Nicholas Broad
• Hello, Claude! 1000 essays from Anthropic... by Darragh

as well as the original competition training dataset

• Version 1:This dataset is composed of 13,712 human texts and 1165 AI-LLM generated texts originating from 7 of the PERSUADE 2.0 corpus prompts.

A company has a fleet of devices transmitting daily sensor readings. They would like to create a predictive maintenance solution to proactively identify when maintenance should be performed. This approach promises cost savings over routine or time based preventive maintenance, because tasks are performed only when warranted.

The task is to build a predictive model using machine learning to predict the probability of a device failure. When building this model, be sure to minimize false positives and false negatives. The column you are trying to Predict is called failure with binary value 0 for non-failure and 1 for failure.

The Website Screenshots dataset is a synthetically generated dataset composed of screenshots from over 1000 of the world's top websites. They have been automatically annotated to label the following classes: :fa-spacer: * button - navigation links, tabs, etc. * heading - text that was enclosed in <h1> to <h6> tags. * link - inline, textual <a> tags. * label - text labeling form fields. * text - all other text. * image - <img>, <svg>, or <video> tags, and icons. * iframe - ads and 3rd party content.

Example

This is an example image and annotation from the dataset: https://i.imgur.com/mOG3u3Z.png" alt="WIkipedia Screenshot">

Usage

Annotated screenshots are very useful in Robotic Process Automation. But they can be expensive to label. This dataset would cost over $4000 for humans to label on popular labeling services. We hope this dataset provides a good starting point for your project. Try it with a model from our model library.

The dataset contains 1689 train data, 243 test data and 483 valid data.

Dataset

This dataset was created by Nina Luquez

Contents