Omkar2118/datasets huggingface dataset:
RTE
Full name: Recognizing Textual Entailment

The **Recognizing Textual Entailment (RTE)** datasets come from a series of textual entailment challenges. Data from RTE1, RTE2, RTE3 and RTE5 is combined. Examples are constructed based on news and Wikipedia text.
MRPC
Full name: Microsoft Research Paraphrase Corpus

Microsoft Research Paraphrase Corpus (MRPC) is a corpus consists of 5,801 sentence pairs collected from newswire articles. Each pair is labelled if it is a paraphrase or not by human annotators. The whole set is divided into a training subset (4,076 sentence pairs of which 2,753 are paraphrases) and a test subset (1,725 pairs of which 1,147 are paraphrases).

Source: [Exploiting Semantic Annotations and Q-Learning for Constructing an Efficient Hierarchy/Graph Texts Organization](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4313059/)
Image Source: [https://www.aclweb.org/anthology/I05-5002.pdf](https://www.aclweb.org/anthology/I05-5002.pdf)

Introducing paper: Automatically Constructing a Corpus of Sentential Paraphrases
MemexQA

A large, realistic multimodal dataset consisting of real personal photos and crowd-sourced questions/answers.

Source: [MemexQA: Visual Memex Question Answering](/paper/memexqa-visual-memex-question-answering)

Introducing paper: MemexQA: Visual Memex Question Answering
SIXray

The **SIXray** dataset is constructed by the Pattern Recognition and Intelligent System Development Laboratory, University of Chinese Academy of Sciences. It contains 1,059,231 X-ray images which are collected from some several subway stations. There are six common categories of prohibited items, namely, gun, knife, wrench, pliers, scissors and hammer. It has three subsets called SIXray10, SIXray100 and SIXray1000, There are image-level annotations provided by human security inspectors for the whole dataset. In addition the images in the test set are annotated with a bounding-box for each prohibited item to evaluate the performance of object localization.

Source: [https://github.com/MeioJane/SIXray](https://github.com/MeioJane/SIXray)
Image Source: [https://github.com/MeioJane/SIXray](https://github.com/MeioJane/SIXray)

Introducing paper: SIXray : A Large-scale Security Inspection X-ray Benchmark for Prohibited Item Discovery in Overlapping Images
Freiburg Forest
Full name: Freiburg Forest

The **Freiburg Forest** dataset was collected using a Viona autonomous mobile robot platform equipped with cameras for capturing multi-spectral and multi-modal images. The dataset may be used for evaluation of different perception algorithms for segmentation, detection, classification, etc. All scenes were recorded at 20 Hz with a camera resolution of 1024x768 pixels. The data was collected on three different days to have enough variability in lighting conditions as shadows and sun angles play a crucial role in the quality of acquired images. The robot traversed about 4.7 km each day. The dataset creators provide manually annotated pixel-wise ground truth segmentation masks for 6 classes: Obstacle, Trail, Sky, Grass, Vegetation, and Void.

Source: [http://deepscene.cs.uni-freiburg.de/](http://deepscene.cs.uni-freiburg.de/)
Image Source: [http://deepscene.cs.uni-freiburg.de/](http://deepscene.cs.uni-freiburg.de/)
CoNLL04

The CoNLL04 dataset is a benchmark dataset used for relation extraction tasks. It contains 1,437 sentences, each of which has at least one relation. The sentences are annotated with information about entities and their corresponding relation types.
Nottingham
Full name: Nottingham

The **Nottingham** Dataset is a collection of 1200 American and British folk songs.

Source: [Rethinking Recurrent Latent Variable Model for Music Composition](https://arxiv.org/abs/1810.03226)
Image Source: [https://highnoongmt.wordpress.com/2018/10/02/going-to-use-the-nottingham-music-database/](https://highnoongmt.wordpress.com/2018/10/02/going-to-use-the-nottingham-music-database/)
Cluttered Omniglot

Dataset for one-shot segmentation.

Source: [One-Shot Segmentation in Clutter](/paper/one-shot-segmentation-in-clutter)

Introducing paper: One-Shot Segmentation in Clutter
Ego2Top

Contains annotated egocentric and top-view videos.

Introducing paper: Ego2Top: Matching Viewers in Egocentric and Top-view Videos
WNLI
Full name: Winograd NLI

The WNLI dataset is a part of the GLUE benchmark used for Natural Language Inference (NLI). It contains pairs of sentences, and the task is to determine whether the second sentence is an entailment of the first one or not. The dataset is used to train and evaluate models on their ability to understand these relationships between sentences.