datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
tau2-bench-verified-airline
tau2-bench-verified — airline domain (mirror)
Mirror of the airline domain from
amazon-agi/tau2-bench-verified
(MIT License), pinned at commit 864350a8971a8f8ee9e7b8472e2edc380a806b0c.
Re-hosted for the OpenRouter native TypeScript benchmark harness so it can fetch
the verified airline tasks + environment DB at runtime.
Contents
tasks/test.jsonl — 50 verified airline tasks. Each row has a single
task_json string column holding one verbatim tau2 v2 task object
(id… See the full description on the dataset page: https://huggingface.co/datasets/abhinavpola/tau2-bench-verified-airline.twitter-airline-sentiment
Dataset Card for Twitter US Airline Sentiment
Dataset Summary
This data originally came from Crowdflower's Data for Everyone library.
As the original source says,
A sentiment analysis job about the problems of each major U.S. airline. Twitter data was scraped from February of 2015 and contributors were asked to first classify positive, negative, and neutral tweets, followed by categorizing negative reasons (such as "late flight" or "rude service").
The data we're… See the full description on the dataset page: https://huggingface.co/datasets/osanseviero/twitter-airline-sentiment.dot-airline-ontime
DOT Airline On-Time Performance 2018–2024
45,968,068 reported flight records across all 84 months, with all 109 BTS source fields, exact delay values and separate cancellation/diversion outcomes.
This repository contains the 1,000-row public sample, covering all 84 months.
The full package is a one-time $99 snapshot, with monthly CSV and Parquet files.
It has 111 Parquet columns: 109 BTS fields plus source_month and source_row.
The deterministic sample uses 12 evenly spaced rows… See the full description on the dataset page: https://huggingface.co/datasets/claritystorm/dot-airline-ontime.airline
airline
An executable Environment for tool-using agents, rebuilt from traces by Kullback and published by Leibler: the world, the Tasks and a code Verifier per Task, no recordings. tasks.jsonl lists the Tasks.
Release: replays at least 90% of its Tasks.
Fidelity over Tasks
100.0% (130 of 130)
Fidelity over Runs
99.5% (199 of 200)
Call fidelity
99.93% of 1513
Reference confirmed
130
Verifier derived
128
Trusted
82
Refused
0
Not trusted
48 of 130; 17 the… See the full description on the dataset page: https://huggingface.co/datasets/leibler/airline.airline-vision-dataset
Airline Industry VQA Dataset
⚠️ Note: This dataset currently contains only the text data (questions). The images are being processed and will be added in a future update.
This dataset contains a comprehensive collection of visual question-answering (VQA) pairs generated from official documentation of 18 major airline companies.
About the Creator
I'm David Soeiro-Vuong, an engineering student specializing in Computer Science, Big Data, and AI, currently working as an… See the full description on the dataset page: https://huggingface.co/datasets/Davidsv/airline-vision-dataset.US_Airline_SentimentTau2-Bench-Airline-With-Code-Agents
Dataset Card for a Code Agent Version of Tau Bench 2 Airline
Dataset Summary
This dataset includes sample traces and associated metadata from multi-turn interactions between an code agent and AI assistant. The dataset is based on the Airline environment from Tau^2 Bench and contains traces from both the original version and a version made at Snorkel AI using code agents to solve the same tasks (indicator in the version field; details below).
Curated by: Snorkel AI… See the full description on the dataset page: https://huggingface.co/datasets/snorkelai/Tau2-Bench-Airline-With-Code-Agents.airline-satisfaction-analysis
Airline Passenger Satisfaction – EDA Report
This project analyzes the Airline Passenger Satisfaction Dataset, containing 103,904 rows and 25 columns describing passenger demographics, flight information, and service ratings.The goal is to understand which factors influence satisfaction, identify important service features,and compare satisfaction between different traveler types and flight classes.
Dataset Overview
The dataset includes:
Passenger demographics (age… See the full description on the dataset page: https://huggingface.co/datasets/drukeroni/airline-satisfaction-analysis.Tau2-Bench-Verified-Airline-With-Code-Agents
Dataset Card for a Code Agent Version of Tau Bench 2 Airline
Dataset Summary
This dataset includes sample traces and associated metadata from multi-turn interactions between an code agent and AI assistant, along with the original verion of the tasks with more bespoke tools.
The dataset is based on a verified version of the Airline environment from Sierra.ai's Tau^2 Bench with the verified version from Amazon AGI group here.
You can find an earlier version of the dataset… See the full description on the dataset page: https://huggingface.co/datasets/snorkelai/Tau2-Bench-Verified-Airline-With-Code-Agents.us-airline-aerospace-defense-layoffs-warn-act-notices-daily
US airline, aerospace and defense layoffs — the actual WARN Act filings, rebuilt every day
Last rebuilt: 2026-09-24. 1,551 layoff and closure notices filed by
airlines and regional carriers, airport ground-handling and catering contractors, aircraft and engine makers, avionics and airfoil shops, and defense and space primes and their suppliers with US state labor departments — 308,032 workers,
301 employers, 45 states, 1989–2026.
172 of the notices (11.1%) were recorded by the… See the full description on the dataset page: https://huggingface.co/datasets/APProjects/us-airline-aerospace-defense-layoffs-warn-act-notices-daily.airline-voice-concise
airline-voice-concise
Made with the whileai SDK · Used by: recipes/community/airline-voice-concise-under-probe-outcome-filter · Collection: Register
Training data for putting a speaking register into a model's weights. An
airline support agent that leads with the answer and stops, trained so the
register survives with no instruction in the prompt.
Trained on this set, Qwen3-4B goes from 2.2% to 92.1% of held-out replies
in the register, and becomes less likely to omit required… See the full description on the dataset page: https://huggingface.co/datasets/while-ai/airline-voice-concise.airline-resist-jailbreaks
airline-resist-jailbreaks
Made with the whileai SDK · Collection: Robustness
Jailbreak resistance for a customer support agent, trained on simulated
attacks and tested on real ones.
The real attacks come from elder-plinius/L1B3RT4S,
a public library of working jailbreaks. We read it to extract the attack
techniques and never trained on a single string from it. It is the
evaluation set, unseen by the model.
On 165 unseen blocks from a public jailbreak library the agent holds its… See the full description on the dataset page: https://huggingface.co/datasets/while-ai/airline-resist-jailbreaks.tau2-airline-deepseek-distill
τ²-bench airline · DeepSeek teacher trajectories
Successful multi-turn agent trajectories on τ²-bench's
airline domain, generated by running DeepSeek V4 Flash as the agent through the real τ²-bench
harness — same system prompt, same 14 tool schemas, same dialogue loop, same evaluator.
Used to behavior-clone the RL warm start
yuyu0529nya/qwen2.5-7b-tau2-airline-sft-lora,
which is the step 0 of the tau2_airline verl recipe.
Why these exist
GRPO on τ²-bench-airline… See the full description on the dataset page: https://huggingface.co/datasets/yuyu0529nya/tau2-airline-deepseek-distill.airlines-sentiment-analysisaprm-sft-thoughts-tau2-airline-policy_best-adamw30-lp0
Act-PRM SFT thoughts — tau2-bench airline
Act-PRM (Action Process Reward Models) infers the latent thoughts behind
logged, action-only agent demonstrations via an offline EM. For each
logged action x in state s we sample G=4 candidate thoughts z,
score each by the length-penalized action likelihood
reward(z) = p(x | s, z)
(len_frac grows with the thought's token length), and mark the best thought
(argmax reward). The (thought + action) span is then what downstream SFT / RL… See the full description on the dataset page: https://huggingface.co/datasets/mzio/aprm-sft-thoughts-tau2-airline-policy_best-adamw30-lp0.twitter_us_airline_sentiment
Twitter US Airline Sentiment Dataset
This dataset contains tweets about US airlines labeled with sentiment (negative, neutral, positive).
Dataset Details
Total tweets: 14,640
Training set: 11,712 tweets
Validation set: 1,464 tweets
Test set: 1,464 tweets
Classes: negative (0), neutral (1), positive (2)
Files
train.jsonl: Training data (11,712 examples)
validation.jsonl: Validation data (1,464 examples)
test.jsonl: Test data (1,464 examples)
Usage… See the full description on the dataset page: https://huggingface.co/datasets/viethq1906/twitter_us_airline_sentiment.airline-otp-datakaggle-airline-delayairline_satisfactionairline-delay
U.S. Airline On-Time Performance and Delay Causes, 2009-2018
This dataset contains backend-ready Parquet data derived exclusively from the
120 monthly Reporting Carrier On-Time Performance archives published by the
U.S. Department of Transportation, Bureau of Transportation Statistics (BTS),
for January 2009 through December 2018.
It provides two configurations:
aggregate (default): monthly reporting-carrier and destination-airport
aggregates for dashboards, APIs, and… See the full description on the dataset page: https://huggingface.co/datasets/Donny-Guo/airline-delay.aprm-sft-thoughts-tau2-airline-base_best-adamw30-lp0
Act-PRM SFT thoughts — tau2-bench airline
Act-PRM (Action Process Reward Models) infers the latent thoughts behind
logged, action-only agent demonstrations via an offline EM. For each
logged action x in state s we sample G=4 candidate thoughts z,
score each by the length-penalized action likelihood
reward(z) = p(x | s, z)
(len_frac grows with the thought's token length), and mark the best thought
(argmax reward). The (thought + action) span is then what downstream SFT / RL… See the full description on the dataset page: https://huggingface.co/datasets/mzio/aprm-sft-thoughts-tau2-airline-base_best-adamw30-lp0.basic-airline-9c620f
basic-airline-9c620f
Synthetic weather test data: 49 rows in data.csv.
All values are randomly generated fictional examples, not real observations, products, or user activity. Intended only for CSV loading and pipeline tests; not suitable for scientific or business conclusions. Columns are sampled independently and do not model real-world correlations.
Fields
sample_id: random identifier for this generated sample.
row_id: sequential row number starting at 1.… See the full description on the dataset page: https://huggingface.co/datasets/SableKiteA/basic-airline-9c620f.pet-airlines
Pet Air Travel Dataset (Japan Outbound)
A structured, machine-readable dataset of pet travel policies for major international airlines, focused on flights departing from Japan. Designed to be directly consumable by AI agents and downstream applications without scraping.
Overview
The web is full of human-readable pet travel guides; almost none are usable by an AI agent or a typed application without per-airline scraping. pet_airlines provides the missing structured… See the full description on the dataset page: https://huggingface.co/datasets/Aulvem/pet-airlines.tweet-sentiment-airlines
Dataset Card for "tweet-sentiment-airlines"
More Information needed
airline-disruption-dataairline_DPOasia-owid-number-airline-passengers
Number Airline Passengers | Asia (Our World in Data)
🌏 2,142 observations · 46 Asia countries · 1970–2023 · Repackaged by Electric Sheep Asia
TL;DR
This dataset contains 2,142 observations of Number Airline Passengers data across 46 Asia countries, spanning 1970–2023.
About the source
Source: Our World in Data
Publisher: Our World in Data
License: cc-by-4.0
Topic: Number Airline Passengers
Geographic coverage
46 Asia countries… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepasia/asia-owid-number-airline-passengers.US_airline_datasetaprm-thought-generations-tau2-airline
Act-PRM Thought Generations — tau2-bench Airline
Latent thoughts inferred behind logged, action-only agent demonstrations by the
Act-PRM offline EM (Action Process Reward Models), for the tau2-bench airline
domain. For each logged (state s, action x) the EM-trained generator samples
G=4 candidate thoughts z, and each candidate is scored by the
length-penalized action likelihood of the logged action:
likelihood = p(x | s, z) # policy per-action-token… See the full description on the dataset page: https://huggingface.co/datasets/mzio/aprm-thought-generations-tau2-airline.abdelazizel7or_airline-delay-cause
Airline Delay Cause
Why your flight was late: 20 years of U.S. airline delays
Dataset Info
Source: Kaggle
Original Size: 10.49 MB
Kaggle Downloads: 2,489
Files: 1
Files
Airline_Delay_Cause.csv
Mirrored from Kaggle