Omkar2118/datasets huggingface dataset:
SemEval-2017 Task-10

We describe the SemEval task of extracting keyphrases and relations between them from scientific documents, which is crucial for understanding which publications describe which processes, tasks and materials. Although this was a new task, we had a total of 26 submissions across 3 evaluation scenarios. We expect the task and the findings reported in this paper to be relevant for researchers working on understanding scientific content, as well as the broader knowledge base population and information extraction communities.

Introducing paper: SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications
Inspec

Paper: Improved automatic keyword extraction given more linguistic knowledge
Doi: [10.3115/1119355.1119383](https://doi.org/10.3115/1119355.1119383)
Essays
Full name: Stream-of-consciousness Essays

J. W. Pennebaker and L. A. King, “Linguistic styles: Language use as an individual difference,” J. Pers. Soc. Psychol., vol. 77, no. 6, pp. 1296–1312, Dec. 1999, doi: [10.1037/0022-3514.77.6.1296](https://psycnet.apa.org/doi/10.1037/0022-3514.77.6.1296).
MIT Indoor Scenes

Context
This is the Original data provided by MIT .

Indoor scene recognition is a challenging open problem in high level vision. Most scene recognition models that work well for outdoor scenes perform poorly in the indoor domain. The main difficulty is that while some indoor scenes (e.g. corridors) can be well characterized by global spatial properties, others (e.g., bookstores) are better characterized by the objects they contain. More generally, to address the indoor scenes recognition problem we need a model that can exploit local and global discriminative information.

Content
The database contains 67 Indoor categories, and a total of 15620 images. The number of images varies across categories, but there are at least 100 images per category. All images are in jpg format. The images provided here are for research purposes only.

Acknowledgements
Thanks to MIT
Thanks to Aude Oliva for helping to create the database of indoor scenes.
Funding for this research was provided by NSF Career award (IIS 0747120)
Intel Image Classification

Context
This is image data of Natural Scenes around the world.

Content
This Data contains around 25k images of size 150x150 distributed under 6 categories.
{'buildings' -> 0,
'forest' -> 1,
'glacier' -> 2,
'mountain' -> 3,
'sea' -> 4,
'street' -> 5 }

The Train, Test and Prediction data is separated in each zip files. There are around 14k images in Train, 3k in Test and 7k in Prediction.
This data was initially published on https://datahack.analyticsvidhya.com by Intel to host a Image classification Challenge.

Acknowledgements
Thanks to https://datahack.analyticsvidhya.com for the challenge and Intel for the Data

Photo by Jan Böttinger on Unsplash

Inspiration
Want to build powerful Neural network that can classify these images with more accuracy.