Omkar2118/datasets huggingface dataset:
ChAII - Hindi and Tamil Question Answering

The dataset covers Hindi and Tamil, collected without the use of translation. It provides a realistic information-seeking task with questions written by native-speaking expert data annotators.
MC_GRID
Full name: Multi_Channel_Grid

Here we release the dataset (Multi_Channel_Grid, abbreviated as **MC_Grid**) used in our paper [LIMUSE: LIGHTWEIGHT MULTI-MODAL SPEAKER EXTRACTION]([[2111.04063\] LiMuSE: Lightweight Multi-modal Speaker Extraction (arxiv.org)](https://arxiv.org/abs/2111.04063)).

MC_Grid, which is based on [GRID](http://spandh.dcs.shef.ac.uk/gridcorpus/) dataset, includes multi-channel audio, extracted voiceprint and visual feature. The method of feature extraction will be introduced below.

MC_Grid is specially prepared for speaker extraction task, and our code is available at [aispeech-lab/LiMuSE](https://github.com/aispeech-lab/LiMuSE). Feel free to contact us if you have any questions or suggestions.

Introducing paper: LiMuSE: Lightweight Multi-modal Speaker Extraction
Korean Hate Speech Evaluation Datasets

APEACH is the first crowd-generated Korean evaluation dataset for hate speech detection. Sentences of the dataset are created by anonymous participants using an online crowdsourcing platform DeepNatural AI.

Introducing paper: APEACH: Attacking Pejorative Expressions with Analysis on Crowd-Generated Hate Speech Evaluation Datasets
Forest CoverType

Predicting forest cover type from cartographic variables only (no remotely sensed data). The actual forest cover type for a given observation (30 x 30 meter cell) was determined from US Forest Service (USFS) Region 2 Resource Information System (RIS) data. Independent variables were derived from data originally obtained from US Geological Survey (USGS) and USFS data. Data is in raw form (not scaled) and contains binary (0 or 1) columns of data for qualitative independent variables (wilderness areas and soil types).

This study area includes four wilderness areas located in the Roosevelt National Forest of northern Colorado. These areas represent forests with minimal human-caused disturbances, so that existing forest cover types are more a result of ecological processes rather than forest management practices.

Some background information for these four wilderness areas: Neota (area 2) probably has the highest mean elevational value of the 4 wilderness areas. Rawah (area 1) and Comanche Peak (area 3) would have a lower mean elevational value, while Cache la Poudre (area 4) would have the lowest mean elevational value.

As for primary major tree species in these areas, Neota would have spruce/fir (type 1), while Rawah and Comanche Peak would probably have lodgepole pine (type 2) as their primary species, followed by spruce/fir and aspen (type 5). Cache la Poudre would tend to have Ponderosa pine (type 3), Douglas-fir (type 6), and cottonwood/willow (type 4).

The Rawah and Comanche Peak areas would tend to be more typical of the overall dataset than either the Neota or Cache la Poudre, due to their assortment of tree species and range of predictive variable values (elevation, etc.) Cache la Poudre would probably be more unique than the others, due to its relatively low elevation range and species composition.