datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
datacomp_xlarge
DataComp XLarge Pool
This repository contains metadata files for the xlarge pool of DataComp. For details on how to use the metadata, please visit our website and our github repository.
We distribute the image url-text samples and metadata under a standard Creative Common CC-BY-4.0 license. The individual images are under their own copyrights.
Terms and Conditions
We have terms of service that are similar to those adopted by HuggingFace… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations/datacomp_xlarge.xlam-function-calling-60k
APIGen Function-Calling Datasets
Paper | Website | Models
This repo contains 60,000 data collected by APIGen, an automated data generation pipeline designed to produce verifiable high-quality datasets for function-calling applications. Each data in our dataset is verified through three hierarchical stages: format checking, actual function executions, and semantic verification, ensuring its reliability and correctness.
We conducted human evaluation over 600 sampled data points, and… See the full description on the dataset page: https://huggingface.co/datasets/lockon/xlam-function-calling-60k.xlam-function-calling-60k
APIGen Function-Calling Datasets
Paper | Website | Models
This repo contains 60,000 data collected by APIGen, an automated data generation pipeline designed to produce verifiable high-quality datasets for function-calling applications. Each data in our dataset is verified through three hierarchical stages: format checking, actual function executions, and semantic verification, ensuring its reliability and correctness.
We conducted human evaluation over 600 sampled data points… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k.BRIGHT
BRIGHT benchmark
BRIGHT is the first text retrieval benchmark that requires intensive reasoning to retrieve relevant documents.
The queries are collected from diverse domains (StackExchange, LeetCode, and math competitions), all sourced from realistic human data.
Experiments show that existing retrieval models perform poorly on BRIGHT, where the highest score is only 22.1 measured by nDCG@10.
BRIGHT provides a good testbed for future retrieval research in more realistic and… See the full description on the dataset page: https://huggingface.co/datasets/xlangai/BRIGHT.xlam-function-calling-60k-parsed
[PARSED] APIGen Function-Calling Datasets (xLAM)
This dataset contains the full data from the original Salesforce/xlam-function-calling-60k
Subset name
multi-turn
parallel
multiple definition
Last turn type
number of dataset
xlam-function-calling-60k
no
yes
yes
tool_calls
60000
This is a re-parsing formatting dataset for the xLAM official dataset.
Load the dataset
from datasets import load_dataset
ds =… See the full description on the dataset page: https://huggingface.co/datasets/minpeter/xlam-function-calling-60k-parsed.spider
Dataset Card for Spider
Dataset Summary
Spider is a large-scale complex and cross-domain semantic parsing and text-to-SQL dataset annotated by 11 Yale students.
The goal of the Spider challenge is to develop natural language interfaces to cross-domain databases.
Supported Tasks and Leaderboards
The leaderboard can be seen at https://yale-lily.github.io/spider
Languages
The text in the dataset is in English.
Dataset Structure
Data… See the full description on the dataset page: https://huggingface.co/datasets/xlangai/spider.xlam-function-calling-60kDS-1000 DS-1000 in simplified format
🔥 Check the leaderboard from Eval-Arena on our project page.
See testing code and more information (also the original fill-in-the-middle/Insertion format) in the DS-1000 repo.
Reformatting credits: Yuhang Lai, Sida Wang
xlam-function-calling-60k-hermesxlam-function-calling-60k-raw
XLAM Function Calling 60k Raw Dataset
This dataset includes train and test splits derived from Salesforce/xlam-function-calling-60k.
Train split size: 95% of the original dataset
Test split size: 5% of the original dataset
osworld_v2_tasks
OSWorld V2 Task Classes
This gated dataset contains the official root-level task_*.py Python task classes for OSWorld V2.
The public GitHub repository keeps the task loader, helper utilities, and documentation. The task implementations are gated to reduce benchmark leakage and to help prevent evaluated agents from finding task answers, setup logic, or evaluator details online while executing a task.
Download from the public repository root with:
uvx --from huggingface_hub hf… See the full description on the dataset page: https://huggingface.co/datasets/xlangai/osworld_v2_tasks.CUA-Gym
CUA-Gym
CUA-Gym is a collection of verifiable computer-use agent tasks for reinforcement learning with verifiable rewards (RLVR). Each task pairs a natural-language instruction with executable setup artifacts and a Python reward function that checks task completion programmatically. For details, see the paper CUA-Gym: Scaling Verifiable Training Environments and Tasks for Computer-Use Agents.
This release contains the full public CUA-Gym task set after the necessary data review.… See the full description on the dataset page: https://huggingface.co/datasets/xlangai/CUA-Gym.xlam-function-calling-60k-shareGPTShareGPT converted version of Salesforce/xlam-function-calling-60k
Salesforce-xlam-function-calling-60kxlam-irrelevance-7.5k
xlam-irrelevance-7.5k
Overview
The xlam-irrelevance-7.5k is a specialized dataset designed to activate the ability of irrelevant function detection for large language models (LLMs).
Source and Construction
This dataset is built upon xlam-function-calling-60k dataset, from which we random sampled 7.5k instances, removed the ground truth function from the provided tool list, and relabel them as irrelevant. For more details, please refer to Hammer: Robust… See the full description on the dataset page: https://huggingface.co/datasets/MadeAgents/xlam-irrelevance-7.5k.xlam_hermes_validatedSalesforce-xlam-function-calling-60kWikiHow-taskset(Works with Mobile-Env >=4.0.)
Notice: THE PUBLIC WIKIHOW APK AND CACHED PUBLIC WEBSITE DATA FOR REPRODUCTION
HAVE BEEN REMOVED ACCROING TO THE REQUEST OF WIKIHOW INC.
WikiHow Task Set
WikiHow task set is an InfoUI interaction task set based on
Mobile-Env proposed in Mobile-Env:
Building Qualified Evaluation Benchmarks for LLM-GUI
Interaction.
WikiHow is a collaborative wiki site about
various real-life tips with more than 340,000 online articles. To construct the
task set, 107… See the full description on the dataset page: https://huggingface.co/datasets/X-LANCE/WikiHow-taskset.xlam-function-calling-60k-raw-augmented
XLAM Function Calling 60k Raw Augmented Dataset
This dataset includes augmented train and test splits derived from product-science/xlam-function-calling-60k-raw.
Train split size: Original size plus augmented data
Test split size: Original size plus augmented data
Augmentation Details
This dataset has been augmented by modifying function names in the original data. Randomly selected function names have underscores replaced with periods at random positions… See the full description on the dataset page: https://huggingface.co/datasets/product-science/xlam-function-calling-60k-raw-augmented.AgentTrek
AgentTrek Data Collection
AgentTrek dataset is the training dataset for the Web agent AgentTrek-1.0-32B. It consists of a total of 52,594 dialogue turns, specifically designed to train a language model for performing web-based tasks, such as browsing and web shopping. The dialogues in this dataset simulate interactions where the agent assists users in tasks like searching for information, comparing products, making purchasing decisions, and navigating websites.
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/xlangai/AgentTrek.OpenGrad-ToolPolicy-Canonical-v2-minus-xlam
This is the byte-identical training view for a joint xLAM-plus-CALL_PREDICTION removal
experiment. xLAM is currently the corpus's only source of that supervision contract, so this is
not a pure source-content ablation. It carries no result of its own and is not a recommended
mixture. It is part of OpenGrad Study 001.
What this is
OpenGrad-ToolPolicy-Canonical-v2
with one source removed: xLAM/APIGen. Three sources remain, 115,895 canonical records, 118
shards.
It is the exact… See the full description on the dataset page: https://huggingface.co/datasets/arjhinety/OpenGrad-ToolPolicy-Canonical-v2-minus-xlam.CoT-XLangRU:CoT-XLang — это многоязычный датасет, состоящий из текстовых примеров с пошаговыми рассуждениями (Chain-of-Thought, CoT) на различных языках, включая английский, русский, японский и другие. Он используется для обучения и тестирования моделей в задачах, требующих пояснений решений через несколько шагов. Датасет включает около 2,419,912 примеров, что позволяет эффективно обучать модели, способные генерировать пошаговые рассуждения.
Рекомендация:Используйте датасет для обучения моделей… See the full description on the dataset page: https://huggingface.co/datasets/Egor-3926/CoT-XLang.computer-agent-arena
Computer Agent Arena: Evaluating Computer-Use Agents via Crowdsourcing from Real Users
Dataset Description
Computer Agent Arena is a comprehensive evaluation platform for multi-modal AI agents, particularly focusing on computer use and GUI interaction tasks. This dataset contains real interaction trajectories from various state-of-the-art AI agents performing complex computer tasks in controlled environments.
The dataset includes:
4,641 agent trajectories across diverse… See the full description on the dataset page: https://huggingface.co/datasets/xlangai/computer-agent-arena.xlam-function-calling-60k_langchainReformatted dataset from "Salesforce/xlam-function-calling-60k" (from Hugging Face) for the purposes of fine tuning LLMs for tool calling for the LangChain and LangGraph frameworks
license: mit
spider2-litexlam-ru-tool-callingsalesforce-xlam-finetune-normalxlam-function-calling-processedsommelier-xlam-single-call-splits
sommelier xlam single-call splits
Deterministic, deduplicated, single-tool-call train/validation/test
splits derived from
Salesforce/xlam-function-calling-60k
(APIGen, CC-BY-4.0), produced by the
sommelier pipeline for
reproducible tool-calling fine-tuning. These are the exact splits used to
train and evaluate
abdelstark/llama-3.1-nemotron-nano-8b-xlam-tool-calling-lora.
Why single-call
The upstream dataset mixes single-call and multi-call examples (~52.6%… See the full description on the dataset page: https://huggingface.co/datasets/abdelstark/sommelier-xlam-single-call-splits.Salesforce-xlam-function-calling-60k-BMO-FORMAT