Omkar2118/datasets huggingface dataset:
bFFHQ
Full name: Gender-biased FFHQ dataset

Gender-biased FFHQ dataset (bFFHQ) has age as a target label and gender as a correlated bias, and the images are from the FFHQ dataset. The images include the dominant number of young women (i.e., aged 10-29) and old men (i.e., aged 40-59) in the training data.

Source: [Learning Debiased Representation via Disentangled Feature Augmentation](https://github.com/kakaoenterprise/Learning-Debiased-Disentangled)
Image Source: [FFHQ](https://github.com/NVlabs/ffhq-dataset)

Introducing paper: BiaSwap: Removing dataset bias with bias-tailored swapping augmentation
Vehicle Claims

The code to create the dataset is available [here](https://github.com/ajaychawda58/UADAD/blob/main/Code/Notebooks/create_dataset.ipynb).
The dataset used in the paper is available on [github](https://github.com/ajaychawda58/UADAD/tree/main/data/vehicle_claims)

- `Maker` - *Categorical* - The brand of the vehicle.
- `GenModel` - *Categorical* - The model of the vehicle.
- `Color` - *Categorical* - Colour of the vehicle.
- `Reg_Year` - *Categorical* - Year of Registration.
- `Body_Type` - *Categorical* - Eg. SUV, Convertible.
- `Runned_Miles` - *Numerical* - Distance covered by the vehicle.
- `Engin_Size` - *Categorical* - Size of engine.
- `GearBox` - *Categorical* - Automatic, Manual.
- `FuelType` - *Categorical* - Petrol, Diesel.
- `Price` -  *Numerical* - Price of vehicle.
- `Seat_num` - *Numerical* - Number of seats.
- `Door_num` -  *Numerical* - Number of Doors.
- `issue` - *Categorical* - Type of damage.
- `issue_id` - *Categorical* - Specific damage.
- `repair_complexity` - *Categorical* - Difficulty to repair the vehicle.
- `repair_hours` -  *Numerical* - Time required to finish the job.
- `repair_cost` - *Numerical* - Cost of repair.

Other attributes are not used for evaluation in this work. 
`breakdown_date` and `repair_date` were added with the idea of inserting anomalies based on the number of days required to repair the vehicle.

Introducing paper: Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical Encodings
S-TEST

S-TEST is a benchmark for measuring the specificity of the language of pre-trained language models.

Introducing paper: Can Language Models Be Specific? How?
Open Relation Modeling

Given two entities, generating a coherent sentence describing the relation between them.

E.g., (data mining, database) => data mining is a process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.

Introducing paper: Open Relation Modeling: Learning to Define Relations between Entities
LM Email Address Leakage

Are Large Pre-Trained Language Models Leaking Your Personal Information? We analyze whether Pre-Trained Language Models (PLMs) are prone to leaking personal information. Specifically, we query PLMs for email addresses with contexts of the email address or prompts containing the owner's name.

Introducing paper: Are Large Pre-Trained Language Models Leaking Your Personal Information?