datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
lambada_openai
Dataset Summary
This dataset is comprised of the LAMBADA test split as pre-processed by OpenAI (see relevant discussions here and here). It also contains machine translated versions of the split in German, Spanish, French, and Italian.
LAMBADA is used to evaluate the capabilities of computational models for text understanding by means of a word prediction task. LAMBADA is a collection of narrative texts sharing the characteristic that human subjects are able to guess their last word… See the full description on the dataset page: https://huggingface.co/datasets/EleutherAI/lambada_openai.lambada
Dataset Card for LAMBADA
Dataset Summary
The LAMBADA evaluates the capabilities of computational models
for text understanding by means of a word prediction task.
LAMBADA is a collection of narrative passages sharing the characteristic
that human subjects are able to guess their last word if
they are exposed to the whole passage, but not if they
only see the last sentence preceding the target word.
To succeed on LAMBADA, computational models cannot
simply rely on local… See the full description on the dataset page: https://huggingface.co/datasets/cimec/lambada.pokemon-blip-captions
Notice of DMCA Takedown Action
We have received a DMCA takedown notice from The Pokémon Company International, Inc.
In response to this action, we have taken down the dataset.
We appreciate your understanding.
total-300-lambda02-s_signal_type6-jh-epoch4
total-300-lambda02-s_signal_type6-jh-epoch4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.4046875
Action score: 0.4140625
Valid samples: 320/320
total-300-lambda00-s_signal_type6-jh-epoch4
total-300-lambda00-s_signal_type6-jh-epoch4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.3875
Action score: 0.43125
Valid samples: 320/320
total-300-lambda05-s_signal_type6-jh-epoch4
total-300-lambda05-s_signal_type6-jh-epoch4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.35703125
Action score: 0.4375
Valid samples: 320/320
total-300-lambda08-s_signal_type6-jh-epoch4
total-300-lambda08-s_signal_type6-jh-epoch4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.38046875
Action score: 0.4078125
Valid samples: 320/320
total-300-lambda10-s_signal_type6-jh-epoch4
total-300-lambda10-s_signal_type6-jh-epoch4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.36640625
Action score: 0.41875
Valid samples: 320/320
total-300noapp-lambda02-s_signal_type6-jh-epoch4
total-300noapp-lambda02-s_signal_type6-jh-epoch4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.36640625
Action score: 0.409375
Valid samples: 320/320
total-300app-lambda02-s_signal_type6-jh-epoch4
total-300app-lambda02-s_signal_type6-jh-epoch4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.3625
Action score: 0.4015625
Valid samples: 320/320
total-131-lambda02-residual-s_signal_type6-jh-epoch4
total-131-lambda02-residual-s_signal_type6-jh-epoch4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.3765625
Action score: 0.4171875
Valid samples: 320/320
total-300-lambda02-s_signal_type6-jh-retry-epoch4
total-300-lambda02-s_signal_type6-jh-retry-epoch4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.36953125
Action score: 0.3984375
Valid samples: 320/320
total-300-lambda02-s_signal_type6-jh-epoch4-reeval2
total-300-lambda02-s_signal_type6-jh-epoch4-reeval2
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.4125
Action score: 0.4265625
Valid samples: 320/320
total-300-lambda02-s_signal_type6-jh-epoch4-reeval1
total-300-lambda02-s_signal_type6-jh-epoch4-reeval1
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.38828125
Action score: 0.4234375
Valid samples: 320/320
ChinaTravel
ChinaTravel Query Dataset
This dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).
ChinaTravel is an open-ended travel-planning benchmark with compositional
constraint validation for language agents. See the
paper,
Hugging Face paper page,
code, and
bilingual sandbox database
(ModelScope mirror)
for the complete benchmark resources.
Introduction
For a given query, a language agent uses the sandbox tools to collect
information and… See the full description on the dataset page: https://huggingface.co/datasets/LAMDA-NeSy/ChinaTravel.hermes-agent-reasoning-traces
Hermes Agent Reasoning Traces
Multi-turn tool-calling trajectories for training AI agents using the Hermes Agent harness. Each sample is a real agent conversation with step-by-step reasoning (<think> blocks) and actual tool execution results.
This dataset has two configs, one per source model:
Config
Model
Samples
kimi
Moonshot AI Kimi-K2.5
7,646
glm-5.1
ZhipuAI GLM-5.1-FP8
7,055
Loading
from datasets import load_dataset
# Kimi-K2.5 traces
ds =… See the full description on the dataset page: https://huggingface.co/datasets/lambda/hermes-agent-reasoning-traces.LAMDA
LAMDA: A Longitudinal Android Malware Dataset for Drift Analysis
This dataset contains a longitudinal benchmark for Android malware detection designed to analyze and evaluate concept drift in machine learning models. It includes labeled and feature-engineered Android APK data from 2013 to 2025 (excluding 2015), with over 1 million samples collected from real-world sources.
Dataset Details
LAMDA is the largest and most temporally diverse Android malware dataset to date. It… See the full description on the dataset page: https://huggingface.co/datasets/IQSeC-Lab/LAMDA.m_lamaExtension/Modification of the original m_lama dataset
LAMDA
LAMDA: A Longitudinal Android Malware Dataset for Drift Analysis
This dataset contains a longitudinal benchmark for Android malware detection designed to analyze and evaluate concept drift in machine learning models. It includes labeled and feature-engineered Android APK data from 2013 to 2025 (excluding 2015), with over 1 million samples collected from real-world sources.
Dataset Details
LAMDA is the largest and most temporally diverse Android malware dataset to date. It… See the full description on the dataset page: https://huggingface.co/datasets/PtaSack/LAMDA.lamini_docs
Dataset Card for "lamini_docs"
More Information needed
medical_advice_dialogue_en
Description
The dataset is from medalpaca/medical_meadow_health_advice, formatted as dialogues for speed and ease of use. Many thanks to author for releasing it.
Importantly, this format is easy to use via the default chat template of transformers, meaning you can use huggingface/alignment-handbook immediately, unsloth.
Structure
View online through viewer.
Note
We advise you to reconsider before use, thank you. If you find it useful, please like and… See the full description on the dataset page: https://huggingface.co/datasets/lamhieu/medical_advice_dialogue_en.vocallamoda-fashion-product-images
High-Resolution Fashion Product Images
This dataset is a highly optimized, high-resolution subset of the popular Fashion Product Images Dataset originally hosted on Kaggle.
It contains thousands of unique e-commerce fashion products, combining high-resolution product images with multiple descriptive label attributes.
All low-resolution thumbnails and anomalies have been aggressively filtered out. Every image in this dataset has a minimum resolution of 640px on its shortest… See the full description on the dataset page: https://huggingface.co/datasets/PestoRosso/lamoda-fashion-product-images.openai_lambadaLAMBADA dataset variant used by OpenAI to evaluate GPT-2 and GPT-3.earnings-calls-qa
Lamini Earning Calls QA Dataset
Description
This dataset contains transcripts of earning calls for various companies, along with questions and answers related to the companies' financial performance and other relevant topics.
Format
The transcripts, questions, and answers are in the form of jsonlines files, with each json object in the file containing the transcript of an earning call for a single company.
Data Pipeline Code
The entire data pipeline… See the full description on the dataset page: https://huggingface.co/datasets/lamini/earnings-calls-qa.lambada_openai_de
LAMBADA (DE) — Boldt German Evaluation Suite
A modernized German translation of the LAMBADA benchmark (Paperno et al., 2016), part of the Boldt German Evaluation Suite.
LAMBADA tests a model's ability to track discourse-level context. Each instance consists of a passage where the final word can only be predicted correctly if the model has understood the broader narrative — it cannot be inferred from the final sentence alone. The target word is always the last token of the passage.… See the full description on the dataset page: https://huggingface.co/datasets/Boldt/lambada_openai_de.bigscience-lama
Dataset Card for LAMA: LAnguage Model Analysis - a dataset for probing and analyzing the factual and commonsense knowledge contained in pretrained language models.
@inproceedings{petroni2020how,
title={How Context Affects Language Models' Factual Predictions},
author={Fabio Petroni and Patrick Lewis and Aleksandra Piktus and Tim Rockt{"a}schel and Yuxiang Wu and Alexander H. Miller and Sebastian Riedel},
booktitle={Automated Knowledge Base Construction},
year={2020}… See the full description on the dataset page: https://huggingface.co/datasets/janck/bigscience-lama.LAMDA
LAMDA: A Longitudinal Android Malware Dataset for Drift Analysis
This dataset contains a longitudinal benchmark for Android malware detection designed to analyze and evaluate concept drift in machine learning models. It includes labeled and feature-engineered Android APK data from 2013 to 2025 (excluding 2015), with over 1 million samples collected from real-world sources.
Dataset Details
LAMDA is the largest and most temporally diverse Android malware dataset to date. It… See the full description on the dataset page: https://huggingface.co/datasets/Yanyi10086/LAMDA.bird_text_to_sql
Dataset Card for "bird_text_to_sql"
More Information needed
LaMini-instruction
Dataset Card for "LaMini-Instruction"
Minghao Wu, Abdul Waheed, Chiyu Zhang, Muhammad Abdul-Mageed, Alham Fikri Aji,
Dataset Description
We distill the knowledge from large language models by performing sentence/offline distillation (Kim and Rush, 2016). We generate a total of 2.58M pairs of instructions and responses using gpt-3.5-turbo based on several existing resources of prompts, including self-instruct (Wang et al., 2022), P3 (Sanh et al., 2022), FLAN… See the full description on the dataset page: https://huggingface.co/datasets/MBZUAI/LaMini-instruction.