Dataset Card for Alpaca

I have just performed train, test and validation split on the original dataset. Repository to reproduce this will be shared here soon. I am including the orignal Dataset card as follows.

  Dataset Summary

Alpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's text-davinci-003 engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.… See the full description on the dataset page: https://huggingface.co/datasets/disham993/alpaca-train-validation-test-split.

Train Test Split For Freiburg Dataset In YOLOv7 Format

## Overview

Train Test Split For Freiburg Dataset In YOLOv7 Format is a dataset for object detection tasks - it contains Groceries annotations for 8,879 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

bcsandlund/arc-agi-prompts-train-test-split dataset hosted on Hugging Face and contributed by the HF Datasets community

Pritish92/arc-agi-prompts-train-test-split dataset hosted on Hugging Face and contributed by the HF Datasets community

Node classification on PubMed with 60%/20%/20% random splits for training/validation/test.

Dataset, splits, models, and scripts from the manuscript "When Do Quantum Mechanical Descriptors Help Graph Neural Networks Predict Chemical Properties?" are provided. The curated dataset includes 37 QM descriptors for 64,921 unique molecules across six levels of theory: wB97XD, B3LYP, M06-2X, PBE0, TPSS, and BP86. This dataset is stored in the data.tar.gz file, which also contains a file for multitask constraints applied to various atomic and bond properties. The data splits (training, validation, and test splits) for both random and scaffold-based divisions are saved as separate index files in splits.tar.gz. The trained D-MPNN models for predicting QM descriptors are saved in the models.tar.gz file. The scripts.tar.gz file contains ready-to-use scripts for training machine learning models to predict QM descriptors, as well as scripts for predicting QM descriptors using our trained models on unseen molecules and for applying radial basis function (RBF) expansion to QM atom and bond features.

Below are descriptions of the available scripts:

1. atom_bond_descriptors.sh: Trains atom/bond targets.
2. atom_bond_descriptors_predict.sh: Predicts atom/bond targets from pre-trained model.
3. dipole_quadrupole_moments.sh: Trains dipole and quadrupole moments.
4. dipole_quadrupole_moments_predict.sh: Predicts dipole and quadrupole moments from pre-trained model.
5. energy_gaps_IP_EA.sh: Trains energy gaps, ionization potential (IP), and electron affinity (EA).
6. energy_gaps_IP_EA_predict.sh: Predicts energy gaps, IP, and EA from pre-trained model.
7. get_constraints.py: Generates constraints file for testing dataset. This generated file needs to be provided before using our trained models to predict the atom/bond QM descriptors of your testing data.
8. csv2pkl.py: Converts QM atom and bond features to .pkl files using RBF expansion for use with Chemprop software.

Below is the procedure for running the ml-QM-GNN on your own dataset:

1. Use get_constraints.py to generate a constraint file required for predicting atom/bond QM descriptors with the trained ML models.
2. Execute atom_bond_descriptors_predict.sh to predict atom and bond properties. Run dipole_quadrupole_moments_predict.sh and energy_gaps_IP_EA_predict.sh to calculate molecular QM descriptors.
3. Utilize csv2pkl.py to convert the data from predicted atom/bond descriptors .csv file into separate atom and bond feature files (which are saved as .pkl files here).
4. Run Chemprop to train your models using the additional predicted features supported here.

Complete Final Rainy Dataset With Traintest Split & Augm

## Overview

Complete Final Rainy Dataset With Traintest Split & Augm is a dataset for object detection tasks - it contains Car Auto Motorbike Bus Truck annotations for 2,106 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [MIT license](https://creativecommons.org/licenses/MIT).

Dataset

This dataset was created by IMT2022053

Released under Apache 2.0

Contents

Dataset

This dataset was created by Stiff_Subset

Contents

Including the split of real and null reactions for training, validation and test

Node classification on Film with 60%/20%/20% random splits for training/validation/test.

Dataset

This dataset was created by pascalammeter

Released under MIT

Contents

2084Collective/deepstock-sp500-companies-info-stonkv2-test-train-split dataset hosted on Hugging Face and contributed by the HF Datasets community

Final Raw Rainy Dataset With Augum Without Traintest Split

## Overview

Final Raw Rainy Dataset With Augum Without Traintest Split is a dataset for object detection tasks - it contains Car Auto Motorbike Bus Truck annotations for 731 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This JSON file contains the ground truth annotations for the train and validation set of the DUDE competition (https://rrc.cvc.uab.es/?ch=23&com=tasks) of ICDAR 2023 (https://icdar2023.org/).

V1.0.7 release: 41454 annotations for 4974 documents (train-validation-test)

DatasetDict({
  train: Dataset({
    features: ['docId', 'questionId', 'question', 'answers', 'answers_page_bounding_boxes', 'answers_variants', 'answer_type', 'data_split', 'document', 'OCR'],
    num_rows: 23728
  })
  val: Dataset({
    features: ['docId', 'questionId', 'question', 'answers', 'answers_page_bounding_boxes', 'answers_variants', 'answer_type', 'data_split', 'document', 'OCR'],
    num_rows: 6315
  })
  test: Dataset({
    features: ['docId', 'questionId', 'question', 'answers', 'answers_page_bounding_boxes', 'answers_variants', 'answer_type', 'data_split', 'document', 'OCR'],
    num_rows: 11402
  })
})

++update on answer_type
+++formatting change to answers_variants
++++stricter check on answer_variants & rename annotations file

+ blind test set (no ground truth answers provided)
++ removed duplicates from test set: 

"92bd5c758bda9bdceb5f67c17009207b_ac6964cbdf483e765b6668e27b3d0bc4",

"6ee71a16d4e4d1dbd7c1f569a92d4e08_549f2a163f8ff3e9f0293cf59fdd98bc",

"e6f3855472231a7ca6aada2f8e85fe5a_827c03a72f2552c722f2c872fd7f74c3",

"e3eecd7cca5de11f1d17cd94ae6a8d77_6300df64e4cf6ba0600ac81278f68de2",

"107b4037df8127a92ee4b6ae9b5df8fb_d7a60e7a9fc0b27487ea39cd7f56f98e",

"300cc3900080064d308983f958141232_6a7cf1aad908d58a75ab8e02ddc856f4",

"fdd3308efacddb88d4aa6e2073f481d4_138cb868ecc804a63cc7a4502c0009b2",

"1f7de256ff1743d329a8402ba0d132e7_95b6e8758533a9817b9f20a958e7b776",

"4f399b8c526ffb6a2fd585a18d4ed5ec_51097231bc327c26c59a4fd8d3ff3069",

Split version of the garbage classification dataset (link below). train, test and valid folders have been generated as specified by the one-indexed files of the original dataset

Acknowledgements

Original dataset here: https://www.kaggle.com/asdasdasasdas/garbage-classification

Original dataset https://www.kaggle.com/datasets/bogdancretu/flower299 I choose a Acacia flower as the display picture of this dataset to highlight a problem in the dataset flowers-299, if you go to the second folder of Acacia flowers you will see a bunch of pictures of different looking flowers, despite having different shapes structure and colors they are all technically Acacia flowers but we can't use this data to train because we don't have enough samples of acacia flowers despite all efforts and the best model the probability of a model giving accurate prediction of acacia flowers are low

this set of data needs data augmentation to be effieciently used with resnet50

Dataset

This dataset was created by Bhavesh Padharia

Contents

kanghokh/hak-chat-dataset-train-test-split dataset hosted on Hugging Face and contributed by the HF Datasets community

Juliet-train-split-test-on-BinRealVul

  Dataset Summary

Juliet-train-split-test-on-BinRealVul is a curated subset of the Juliet Test Suite (as organized in the GitHub repository), compiled and lifted to LLVM Intermediate Representation (IR) after pre-process phase. This dataset is designed specifically for training binary vulnerability detection models in a setting that ensures a fair comparison with models trained on CompRealVul_LLVM. The split was constructed to match… See the full description on the dataset page: https://huggingface.co/datasets/CCompote/Juliet-train-split-test-on-BinRealVul.