Omkar2118/datasets huggingface dataset:
Tox21

The **Tox21** data set comprises 12,060 training samples and 647 test samples that represent chemical compounds. There are 801 "dense features" that represent chemical descriptors, such as molecular weight, solubility or surface area, and 272,776 "sparse features" that represent chemical substructures (ECFP10, DFS6, DFS8; stored in Matrix Market Format ). Machine learning methods can either use sparse or dense data or combine them. For each sample there are 12 binary labels that represent the outcome (active/inactive) of 12 different toxicological experiments. Note that the label matrix contains many missing values (NAs). The original data source and Tox21 challenge site is https://tripod.nih.gov/tox21/challenge/.

Source: [Tox21 Machine Learning Data Set](http://bioinf.jku.at/research/DeepTox/tox21.html)
Image Source: [https://www.frontiersin.org/articles/10.3389/fenvs.2015.00080/full](https://www.frontiersin.org/articles/10.3389/fenvs.2015.00080/full)
QM9

**QM9** provides quantum chemical properties (at DFT level) for a relevant, consistent, and comprehensive chemical space of small organic molecules. This database may serve the benchmarking of existing methods, development of new methods, such as hybrid quantum mechanics/machine learning, and systematic identification of structure-property relationships.

Source: [QM9 Dataset](http://quantum-machine.org/datasets/)
Image Source: [https://pubs.acs.org/doi/pdf/10.1021/ci300415d](https://pubs.acs.org/doi/pdf/10.1021/ci300415d)
PCBA

PCBA dataset 11 is a collection of high-quality dose-response data, formulated as a multitask learning benchmark from 128 high-throughput screening (HTS) assays.
MUV

The Maximum Unbiased Validation (MUV) dataset is a benchmark dataset selected from PubChem BioAssay. It was created by applying a refined nearest-neighbor analysis. The MUV dataset is specifically designed for the validation of virtual screening techniques.
Douban
Full name: Douban Conversation Corpus

We release Douban Conversation Corpus, comprising a training data set, a development set and a test set for retrieval based chatbot. The statistics of Douban Conversation Corpus are shown in the following table. 

|      |Train|Val| Test         | 
| ------------- |:-------------:|:-------------:|:-------------:|
| session-response pairs  | 1m|50k| 10k |
| Avg. positive response per session     | 1|1| 1.18    | 
| Fless Kappa | N\A|N\A|0.41      | 
| Min turn per session | 3|3| 3      | 
| Max ture per session | 98|91|45    | 
| Average turn per session | 6.69|6.75|5.95    | 
| Average Word per utterance | 18.56|18.50|20.74   | 


The test data contains 1000 dialogue context, and for each context we create 10 responses as candidates. We recruited three labelers to judge if a candidate is a proper response to the session. A proper response means the response can naturally reply to the message given the context. Each pair received three labels and the majority of the labels was taken as the final decision.

<br>
As far as we known, this is the first human-labeled test set for retrieval-based chatbots. The entire corpus link https://www.dropbox.com/s/90t0qtji9ow20ca/DoubanConversaionCorpus.zip?dl=0

Introducing paper: Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots
Criteo
Full name: Display Advertising Challenge

**Criteo** contains 7 days of click-through data, which is widely used for CTR prediction benchmarking. There are 26 anonymous categorical fields and 13 continuous fields in Criteo dataset.

Source: [AMER: Automatic Behavior Modeling and Interaction Exploration in Recommender System](https://arxiv.org/abs/2006.05933)
Image Source: [https://www.kaggle.com/c/criteo-display-ad-challenge](https://www.kaggle.com/c/criteo-display-ad-challenge)
iPinYou
Full name: iPinYou Global RTB Bidding Algorithm Competition Dataset

The **iPinYou** Global RTB(Real-Time Bidding) Bidding Algorithm Competition is organized by iPinYou from April 1st, 2013 to December 31st, 2013.The competition has been divided into three seasons. For each season, a training dataset is released to the competition participants, the testing dataset is reserved by iPinYou. The complete testing dataset is randomly divided into two parts: one part is the leaderboard testing dataset to score and rank the participating teams on the leaderboard, and the other part is reserved for the final offline evaluation. The participant's last offline submission is evaluated by the reserved testing dataset to get a team's offline final score. This dataset contains all three seasons training datasets and leaderboard testing datasets.The reserved testing datasets are withheld by iPinYou. The training dataset includes a set of processed iPinYou DSP bidding, impression, click, and conversion logs.

Source: [iPinYou Global RTB Bidding Algorithm Competition Dataset](https://contest.ipinyou.com/)
Image Source: [http://contest.ipinyou.com/ipinyou-dataset.pdf](http://contest.ipinyou.com/ipinyou-dataset.pdf)

Introducing paper: iPinYou Global RTB Bidding Algorithm Competition Dataset
AFAD
Full name: Asian Face Age Dataset

The Asian Face Age Dataset (AFAD) is a new dataset proposed for evaluating the performance of age estimation, which contains more than 160K facial images and the corresponding age and gender labels. This dataset is oriented to age estimation on Asian faces, so all the facial images are for Asian faces. It is noted that the AFAD is the biggest dataset for age estimation to date. It is well suited to evaluate how deep learning methods can be adopted for age estimation.

Introducing paper: Ordinal Regression With Multiple Output CNN for Age Estimation
FB15k-237

**FB15k-237** is a link prediction dataset created from FB15k. While FB15k consists of 1,345 relations, 14,951 entities, and 592,213 triples, many triples are inverses that cause leakage from the training to testing and validation splits. FB15k-237 was created by Toutanova and Chen (2015) to ensure that the testing and evaluation datasets do not have inverse relation test leakage. In summary, FB15k-237 dataset contains 310,116 triples with 14,541 entities and 237 relation types.