datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
arxiv_deep_learning_python_research_code_functions_summaries
Dataset Card for "AlgorithmicResearchGroup/arxiv_deep_learning_python_research_code_functions_summaries"
Dataset Description
https://huggingface.co/datasets/AlgorithmicResearchGroup/arxiv_deep_learning_python_research_code_functions_summaries
Dataset Summary
AlgorithmicResearchGroup/arxiv_deep_learning_python_research_code_functions_summaries contains summaries for every python function and class extracted from source code files referenced in ArXiv papers. The… See the full description on the dataset page: https://huggingface.co/datasets/AlgorithmicResearchGroup/arxiv_deep_learning_python_research_code_functions_summaries.arxiv_deep_learning_python_research_code
ArXiv Deep Learning Python Research Code
A curated corpus of Python source code files extracted from GitHub repositories referenced in ArXiv papers. Contains 391,496 files (1.49 GB) filtered to deep learning frameworks, designed for training and evaluating Code LLMs on research-grade code.
Dataset Summary
Statistic
Value
Total files
391,496
Total size
1.49 GB
Source repos
34,099
Time span
ArXiv inception through July 2023
Dataset Structure… See the full description on the dataset page: https://huggingface.co/datasets/AlgorithmicResearchGroup/arxiv_deep_learning_python_research_code.ROCOv2-X-Ray-radiology_-cycle-1
ROCOv2 X-Ray, Report Generation Pilot (Cycle 1)
A 28-row pilot testing whether short ROCO captions can be expanded into report-shaped training pairs.
Why this exists
ROCOv2 captions are one or two clipped sentences, often written to make a teaching point rather than to read as a radiological finding. A vision-language model trained directly on them learns to produce clipped captions, not reports.
This pilot tested a different approach: take the caption and its… See the full description on the dataset page: https://huggingface.co/datasets/deepLEARNING786/ROCOv2-X-Ray-radiology_-cycle-1.ROCOv2-X-Ray-radiology
ROCOv2 X-Ray Subset
Radiographs extracted from ROCOv2 (Radiology Objects in COntext, version 2), for training vision-language models on X-ray interpretation.
What this is
ROCOv2 spans many imaging modalities. This subset keeps only the X-ray studies, so that a model can be trained on a single modality rather than learning across CT, MRI, ultrasound and radiography at once.
Rows
4,254
Split
train
Size
~977 MB
Modality
X-ray only… See the full description on the dataset page: https://huggingface.co/datasets/deepLEARNING786/ROCOv2-X-Ray-radiology.deep_learning_2025This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "omy",
"total_episodes": 50,
"total_frames": 10128,
"total_tasks": 1,
"total_videos": 0,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 20,
"splits": {
"train": "0:50"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/Jeongeun/deep_learning_2025.radimagenet-vqa-500-test
🩺 RadImageNet VQA 500 Test Subset
This dataset contains a curated 500-example test audit subset for Medical Visual Question Answering based on RadImageNet.
📊 Dataset Summary
Total Samples: 500 test VQA pairs
Modalities: CT, MRI, X-ray (Abdomen, Brain, Chest/Lung, Ankle/Foot, Hip, Knee)
Question Types: Open-ended & Closed (Yes/No)
Organization: VQA-DeepLearning
💻 Usage
from datasets import load_dataset
dataset =… See the full description on the dataset page: https://huggingface.co/datasets/VQA-DeepLearning/radimagenet-vqa-500-test.Nvidia-DeepLearningExamplesCode from https://github.com/NVIDIA/DeepLearningExamples
INFO: Found 4341 text files - 2024-Jan-27_02-13
INFO: Train size: 4123
Validation size: 109
Test size: 109
deep-learning-domain-qadataset3deep_learning_books_dataset
Deep Learning Books Dataset
Dataset Information
Features:
page_no: Integer (int64) - Page number in the book.
page_content: String - Text content of the page.
Splits:
train: Training split.
Number of examples: 474
Number of bytes: 1,030,431
Download Size: 509,839 bytes
Dataset Size: 1,030,431 bytes
Dataset Application
This dataset "deep_learning_books_dataset" contains text data from various pages of books related to deep learning.
It can be… See the full description on the dataset page: https://huggingface.co/datasets/Falah/deep_learning_books_dataset.cdg-AICourse-Level3-DeepLearning
Learner & EnfuseBot: Exploring the role of Regularization in Neural Network Training - Generated by Conversation Dataset Generator
This dataset was generated using the Conversation Dataset Generator script available at https://cahlen.github.io/conversation-dataset-generator/.
Generation Parameters
Number of Conversations Requested: 500
Number of Conversations Successfully Generated: 500
Total Turns: 6655
Model ID: meta-llama/Meta-Llama-3-8B-Instruct
Generation Mode:… See the full description on the dataset page: https://huggingface.co/datasets/cahlen/cdg-AICourse-Level3-DeepLearning.vqa-rad
Dataset Card for VQA-RAD
Dataset Description
VQA-RAD is a dataset of question-answer pairs on radiology images. The dataset is intended to be used for training and testing
Medical Visual Question Answering (VQA) systems. The dataset includes both open-ended questions and binary "yes/no" questions.
The dataset is built from MedPix, which is a free open-access online database of medical images.
The question-answer pairs were manually generated by a team of… See the full description on the dataset page: https://huggingface.co/datasets/VQA-DeepLearning/vqa-rad.dataset2Laila
Dataset Card for "Laila"
More Information needed
deeplearning_lmmdataset1deep_learning_books
Dataset Card for "deep_learning_books"
More Information needed
1Lailadataset5lyrics_genre_dataset_mediumLaila_New
Dataset Card for "Laila_New"
More Information needed
dataset4fall2025-deeplearning-noisy-pairs-newfall2025-deeplearning-noisy-pairs-new-2fall2025-deeplearning-noisy-100kfall2025-deeplearning-noisy-test-10fall2025-deeplearning-noisy-pairs-new-colab-1.7