Dataset

This dataset was created by Abhiraj Mandal

Released under Apache 2.0

Contents

Sample hackathon data to practice fraud detection . It has multiple files which will require some thinking to structure and the type of dataset will challenge to find ways to get good accuracy

Ship or vessel detection has a wide range of applications, in the areas of maritime safety, fisheries management, marine pollution, defence and maritime security, protection from piracy, illegal migration, etc. Keeping this in mind, a Governmental Maritime and Coastguard Agency is planning to deploy a computer vision based automated system to identify ship type only from the images taken by the survey boats. You have been hired as a consultant to build an efficient model for this project.

Dataset

This dataset was created by KHUSHI YADAV

Released under Attribution-NonCommercial-ShareAlike 3.0 IGO (CC BY-NC-SA 3.0 IGO)

Contents

Context

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1842206%2Fd9e95121cb5e00a0c6ef76b3f2039470%2F_6ff9a514-feae-4016-a680-5e674c943d14.jpeg?generation=1752462551017569&alt=media" alt="">

Events in & outside Kaggle coinciding +/- 2 days within user registration spikes. Used for MetaKaggle Hackathon

Hackathons are a great way for people to not only learn more about technology but also showcase their existing skills by making projects often in a few hours. This dataset contains data collected from 200 participants of a hackathon conducted for high school students. A lot of columns have been deleted but the remaining columns can be useful to understand the demographic and interests of someone participating in these kind of events.

Dataset

This dataset was created by Bard2024

Released under MIT

Contents

Context

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1842206%2Fe27c4ece1c20108bff7baf4f8dc5a37e%2F_d0311f97-3d66-461c-9af5-ba20d8a9da6f-small.jpeg?generation=1748786488909664&alt=media" alt="">

These are NovaSearch/stella_en_1.5B_v5 embeddings of the Meta Kaggle ForumTopics.csv and ForumMessages.csv

Intended purpose

This is a supplemental dataset for the Meta Kaggle Hackathon

How I preprocessed the text data

1. I removed html elements using BeautifulSoup
2. I replaced any URL value with a placeholder <url> value
3. I removed emojis and symbols
4. I replaced 1 or more carriage returns with just a single white space
5. NovaSearch/stella_en_1.5B_v5 was set to 2048 tokens context size and normalize_embeddings is set to true

Sample Data

The actual text data that I fed into the embedding model can be seen in this dataset

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1842206%2Fe6a47fb262445e7dfefbb7be71d14565%2FScreenshot%20from%202025-06-01%2021-44-28.png?generation=1748785487135090&alt=media" alt="">

How to use

• Download the original csvs from Meta Kaggle dataset so that you can see the original text values and compare it to the preprocessed values.
• You can also just download the samples in the ./sample/*.parquet folder to see how the data looks like, before you download the whole dataset (16GB)
• These are normalized embeddings that you can use with Cosine Similarity

See Related Datasets

• 👉 MetaKaggle Forum Data ALL-MINILM-L12-v2 Embeddings (256 context size| 384 dimensions)
• 👉 MetaKaggle Forum Data BAAI/bge-m3 Embeddings (2048 context size| 1024 dimensions)
• 👉 MetaKaggle Forum Data BGE BASE-EN v1.5 Embeddings (512 context size| 768 dimensions)
• 👉 MetaKaggle Forum Data Jina-Small-Eng-V1 Embeddings (512 context size| 512 dimensions)
• 👉 MetaKaggle Forum Data Qwen2 Embeddings (2048 context size| 1536 dimensions)
• 👉 MetaKaggle Forum Data Stella Embeddings (2048 context size| 1024 dimensions)

Image

Generated with Bing Image Generator

Context

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1842206%2Fef7fe21fba54bb94bff875f3f9820ea5%2F_9e90e8e2-5caf-4214-8726-77afecdaafc1-small.jpeg?generation=1748913590396040&alt=media" alt="">

These are Qwen/Qwen2-1.5B-Instruct embeddings of the Meta Kaggle ForumTopics.csv and ForumMessages.csv

Intended purpose

This is a supplemental dataset for the Meta Kaggle Hackathon

How I preprocessed the text data

1. I removed html elements using BeautifulSoup
2. I replaced any URL value with a placeholder <url> value
3. I removed emojis and symbols
4. I replaced 1 or more carriage returns with just a single white space
5. Qwen/Qwen2-1.5B-Instruct was set to 2048 tokens context size and normalize_embeddings is set to true

Sample Data

The actual text data that I fed into the embedding model can be seen in this dataset

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1842206%2F155f60ac3a36046a5d546283bad80368%2FScreenshot%20from%202025-06-03%2009-20-35.png?generation=1748913654315054&alt=media" alt="">

How to use

• Download the original csvs from Meta Kaggle dataset so that you can see the original text values and compare it to the preprocessed values.
• You can also just download the samples in the ./sample/*.parquet folder to see how the data looks like, before you download the whole dataset (23GB)
• These are normalized embeddings that you can use with Cosine Similarity

See Related Datasets

• 👉 MetaKaggle Forum Data ALL-MINILM-L12-v2 Embeddings (256 context size| 384 dimensions)
• 👉 MetaKaggle Forum Data BAAI/bge-m3 Embeddings (2048 context size| 1024 dimensions)
• 👉 MetaKaggle Forum Data BGE BASE-EN v1.5 Embeddings (512 context size| 768 dimensions)
• 👉 MetaKaggle Forum Data Jina-Small-Eng-V1 Embeddings (512 context size| 512 dimensions)
• 👉 MetaKaggle Forum Data Qwen2 Embeddings (2048 context size| 1536 dimensions)
• 👉 MetaKaggle Forum Data Stella Embeddings (2048 context size| 1024 dimensions)

Image

Generated with Bing Image Generator

Dataset

This dataset was created by Shreya Halgeri

Released under Apache 2.0

Contents

Context

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1842206%2Fa052924b64c6ae6cd801dcc917067ea2%2F_ae937b93-d6fb-4985-b526-f6a31c2970c0-small.jpeg?generation=1749029365407793&alt=media" alt="">

These are jinaai/jina-embedding-s-en-v1 embeddings of the Meta Kaggle ForumTopics.csv and ForumMessages.csv

Intended purpose

This is a supplemental dataset for the Meta Kaggle Hackathon

How I preprocessed the text data

1. I removed html elements using BeautifulSoup
2. I replaced any URL value with a placeholder <url> value
3. I removed emojis and symbols
4. I replaced 1 or more carriage returns with just a single white space
5. jinaai/jina-embedding-s-en-v1 was set to 512 tokens context size and normalize_embeddings is set to true

Sample Data

The actual text data that I fed into the embedding model can be seen in this dataset

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1842206%2F155f60ac3a36046a5d546283bad80368%2FScreenshot%20from%202025-06-03%2009-20-35.png?generation=1748913654315054&alt=media" alt="">

How to use

• Download the original csvs from Meta Kaggle dataset so that you can see the original text values and compare it to the preprocessed values.
• You can also just download the samples in the ./sample/*.parquet folder to see how the data looks like, before you download the whole dataset (23GB)
• These are normalized embeddings that you can use with Cosine Similarity

See Related Datasets

• 👉 MetaKaggle Forum Data ALL-MINILM-L12-v2 Embeddings (256 context size| 384 dimensions)
• 👉 MetaKaggle Forum Data BAAI/bge-m3 Embeddings (2048 context size| 1024 dimensions)
• 👉 MetaKaggle Forum Data BGE BASE-EN v1.5 Embeddings (512 context size| 768 dimensions)
• 👉 MetaKaggle Forum Data Jina-Small-Eng-V1 Embeddings (512 context size| 512 dimensions)
• 👉 MetaKaggle Forum Data Qwen2 Embeddings (2048 context size| 1536 dimensions)
• 👉 MetaKaggle Forum Data Stella Embeddings (2048 context size| 1024 dimensions)

Image

Generated with Bing Image Generator

Dataset

This dataset was created by HoangTran223

Released under Apache 2.0

Contents

Dataset

This dataset was created by Sagar Mondal

Released under MIT

Contents

Dataset

This dataset was created by Gaurav Dutta

Contents

Dataset

This dataset was created by Sarth Mirashi

Contents

Dataset

This dataset was created by Sahabudin Ali

Contents

Dataset

This dataset was created by Alexander Nolte

Contents

Context

This dataset comes from Hackathon Competition: https://tournament.datacrunch.com/how-to-get-started

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Dataset

This dataset was created by Rajat Ranjan

Contents

Dataset

This dataset was created by Mukuliitg

Released under Apache 2.0

Contents