datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
twitter-financial-news-sentiment
Dataset Description
The Twitter Financial News dataset is an English-language dataset containing an annotated corpus of finance-related tweets. This dataset is used to classify finance-related tweets for their sentiment.
The dataset holds 11,932 documents annotated with 3 labels:
sentiments = {
"LABEL_0": "Bearish",
"LABEL_1": "Bullish",
"LABEL_2": "Neutral"
}
The data was collected using the Twitter API. The current dataset supports the multi-class classification… See the full description on the dataset page: https://huggingface.co/datasets/zeroshot/twitter-financial-news-sentiment.ai4sci-virtual-cell-zeroshot-data
ai4sci virtual-cell-zeroshot — release v1
Prepared data for the virtual-cell-zeroshot
task: predict single-cell CRISPRi knockdown responses in cellular contexts a model has never seen
perturbed (the Arc Virtual Cell Challenge 2026 zero-shot setting), rebuilt from public data.
dev/ what the agent sees (mount read-only at /workspace/data)
train/{k562,jurkat,hct116,hek293t}/ cells.h5ad, pseudobulk.h5ad, se_embeddings.npy, dev_split/
test/{ctx_near,ctx_mid… See the full description on the dataset page: https://huggingface.co/datasets/sunweiwei/ai4sci-virtual-cell-zeroshot-data.twitter-financial-news-topic
Dataset Description
The Twitter Financial News dataset is an English-language dataset containing an annotated corpus of finance-related tweets. This dataset is used to classify finance-related tweets for their topic.
The dataset holds 21,107 documents annotated with 20 labels:
topics = {
"LABEL_0": "Analyst Update",
"LABEL_1": "Fed | Central Banks",
"LABEL_2": "Company | Product News",
"LABEL_3": "Treasuries | Corporate Debt",
"LABEL_4": "Dividend"… See the full description on the dataset page: https://huggingface.co/datasets/zeroshot/twitter-financial-news-topic.vsr_zeroshot
VSR: Visual Spatial Reasoning
This is the zero-shot set of VSR: Visual Spatial Reasoning (TACL 2023) [paper].
Usage
from datasets import load_dataset
data_files = {"train": "train.jsonl", "dev": "dev.jsonl", "test": "test.jsonl"}
dataset = load_dataset("cambridgeltl/vsr_zeroshot", data_files=data_files)
Note that the image files still need to be downloaded separately. See data/ for details.
Go to our github repo for more introductions.
Citation
If you find… See the full description on the dataset page: https://huggingface.co/datasets/cambridgeltl/vsr_zeroshot.Zeroshot-Audio-Classification-Instructions
Zeroshot-Audio-Classification-Instructions
Convert audio classification dataset into zero-shot format speech instructions, support both single label and multi-label,
VGGSound
FSD50k
Nonspeech7k
urbansound8K
VocalSound
Emotion
Gender
ESD Emotion
Age
Language
TAU Urban Acoustic Scenes 2022
CochlScene
BirdCLEF_2021
EmoBox
AudioSet
We also converted huge WAV files into MP3 16k sample rate to reduce storage size.To prevent leakage, please do not include test set in training session.… See the full description on the dataset page: https://huggingface.co/datasets/mesolitica/Zeroshot-Audio-Classification-Instructions.synthetic_zeroshot_mixtral_v0.1ProteinGYM-DMS-zeroshotcybersecurity-corpuszero-shot-label-nlitasksource classification tasks recasted as natural language inference.
This dataset is intended to improve label understanding in zero-shot classification HF pipelines.
Inputs that are text pairs are separated by a newline (\n).
from transformers import pipeline
classifier = pipeline(model="sileod/deberta-v3-base-tasksource-nli")
classifier(
"I have a problem with my iphone that needs to be resolved asap!!",
candidate_labels=["urgent", "not urgent", "phone", "tablet", "computer"],
)… See the full description on the dataset page: https://huggingface.co/datasets/tasksource/zero-shot-label-nli.medical-5day-zeroshotsf_fold
SF Fold
Real-world data for general robotics.
🤖 What is SF Fold?
This dataset contains real-world residential t-shirt folding demonstrations, collected by trained data collectors using hand-held grippers in diverse home environments.
📖 Table of Contents
Features
Terminology
Specifications
Dataset Composition
Environment Composition
Garment Composition
Trajectory Specifications
Hardware Specifications
Physical… See the full description on the dataset page: https://huggingface.co/datasets/zeroshotdata/sf_fold.autoui-zeroshot-trajectorieszeroshot_test_downsampledProteinGYM-DMS-RAG-zeroshotso100_test3This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "so100_bimanual",
"total_episodes": 6,
"total_frames": 9213,
"total_tasks": 1,
"total_videos": 12,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:6"
},
"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/ZeroShotLaz/so100_test3.vsr_zeroshot_tsvarxiv-biology
Dataset Curators
The original data is maintained by ArXiv
Licensing Information
The data is under the Creative Commons CC0 1.0 Universal Public Domain Dedication
Citation Information
@misc{clement2019arxiv,
title={On the Use of ArXiv as a Dataset},
author={Colin B. Clement and Matthew Bierbaum and Kevin P. O'Keeffe and Alexander A. Alemi},
year={2019},
eprint={1905.00075},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
so100_bimanualThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "so100_bimanual",
"total_episodes": 5,
"total_frames": 4025,
"total_tasks": 1,
"total_videos": 5,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:5"
},
"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/ZeroShotLaz/so100_bimanual.zero-shot-teacher-feedbackTLDR: Classification + text generation feedback on classroom transcripts.
Is ChatGPT a Good Teacher Coach? Measuring Zero-Shot Performance For Scoring and Providing Actionable Insights on Classroom Instruction
Paper •
Project Page •
Code
Authors: Rose E. Wang and Dorottya Demszky
In the Proceedings of Innovative Use of NLP for Building Educational Applications 2023
Selected as the Ambassador Paper for BEA 2023! 🎉 To be presented at AIED 2024.
If you find… See the full description on the dataset page: https://huggingface.co/datasets/rose-e-wang/zero-shot-teacher-feedback.medical-3day-zeroshot-freshexps-test-no-contextmedical-4day-zeroshot-freshexps-test-no-contextzero_shotmedical-2day-zeroshot-freshexps-testso100_bimanual_coffee2This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "so100_bimanual",
"total_episodes": 6,
"total_frames": 15411,
"total_tasks": 1,
"total_videos": 6,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:6"
},
"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/ZeroShotLaz/so100_bimanual_coffee2.medical-1day-zeroshot-freshexps-test-no-contextzero_shot_open_llm_leaderboardrollout-pi05-zeroshot-stack-two-blocks-2026-02-19This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"robot_type": "franka",
"total_episodes": 5,
"total_frames": 2505,
"total_tasks": 1,
"chunks_size": 1000,
"data_files_size_in_mb": 100,
"video_files_size_in_mb": 200,
"fps": 15,
"splits": {
"train": "0:5"
},
"data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/ankile/rollout-pi05-zeroshot-stack-two-blocks-2026-02-19.medical-1day-zeroshot-freshexps-test-no-context-llama3370binstructagain3PIPer-envbench-zeroshot-rlPaper | Code
zero-shot-emotions-8-4-1.25-85-65-75From the base score of 100, subtract 8 first for Rank 2, then subtract an additional 4 for Rank 3+. For Rank 4+, multiply the subtraction by 1.25, except for the emotions field. Next, divide by 100. Then, for each Rank, multiply this base score by Rank Number If the top original score is less than 65, then penalize all the scores by 75% of the original. If the top score is greater than 85, penalize those that are less than 65.