Omkar2118/datasets huggingface dataset:
ToxiGen

A large-scale and machine-generated dataset of 274,186 toxic and benign statements about 13 minority groups. 

This dataset uses a demonstration-based prompting framework and an adversarial classifier-in-the-loop decoding method to generate subtly toxic and
benign text with a massive pre-trained language model (GPT-3). Controlling machine generation in this way allows TOXIGEN to cover implicitly toxic text at a larger scale,
and about more demographic groups, than previous resources of human-written text. TOXIGEN can be used to fight human-written and machine-generated
toxicity.

Introducing paper: ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection
PET
Full name: PET: A new Dataset for Process Extraction from Natural Language Text

The dataset contains 45 documents containing narrative description of business process and their annotations.  Annotated with activities, gateways, actors, and flow information. 

Each document is composed of three files:

Doc_name.txt (Process description in CONLL format)

Doc_name.process-elements.IOB2.txt (Process elements annotated with IOB2 Schema in CONLL format)

Doc_name.relations.tsv (Process relations between process elements. Each line is a triplette (source, relation tag, target). Source and target are in the form: n_sent_x words range.)

Introducing paper: PET: An Annotated Dataset for Process Extraction from Natural Language Text
PET: A new Dataset for Process Extraction from Natural Language Text

The dataset contains 45 documents containing narrative description of business process and their annotations.  Annotated with activities, gateways, actors, and flow information. 

Each document is composed of three files:

Doc_name.txt (Process description in CONLL format)

Doc_name.process-elements.IOB2.txt (Process elements annotated with IOB2 Schema in CONLL format)

Doc_name.relations.tsv (Process relations between process elements. Each line is a triplette (source, relation tag, target). Source and target are in the form: n_sent_x words range.)

Introducing paper: PET: An Annotated Dataset for Process Extraction from Natural Language Text
EGDB

This dataset contains transcriptions of the electric guitar performance of 240 tablatures, rendered with different tones. The goal is to contribute to automatic music transcription (AMT) of guitar music, a technically challenging task. 

Activity signals were captured by attaching a special hexaphonic pickup to each string of an electric guitar and using a JUCE program to control a digital audio workstation (DAW) to automatically re-render the audio recordings of the “Direct Input” (DI) using different amplifiers (Amps), including low-gain amps and high-gain ones. A new collecting pipeline was employed to reduce the effort of manual inspection. The final dataset contains six copies of 118 minutes of guitar playing, each copy being associated with a different timbre. The new dataset, named “EGDB,” is constructed in this way to account for the diverse timbre associated with electric guitar. Having multiple guitar tones makes it possible to test a trained model
on held-out unseen tones for generalizability. 

<span style="color:grey; opacity: 0.6">( Image Source: [Frame Harirak](https://unsplash.com/s/photos/guitar-electric?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) )</span>

Introducing paper: towards automatic transcription of polyphonic electric guitar music:a new dataset and a multi-loss transformer model