datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.APIGen-MT-5k
Summary
APIGen-MT is an automated agentic data generation pipeline designed to synthesize verifiable, high-quality, realistic datasets for agentic applications
This dataset was released as part of APIGen-MT: Agentic PIpeline for Multi-Turn Data Generation via Simulated Agent-Human Interplay
Code: https://github.com/apigen-mt/apigen-mt.github.io
The repo contains 5000 multi-turn trajectories collected by APIGen-MT
This dataset is a subset of the data used to train the xLAM-2 model… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/APIGen-MT-5k.CRMArenaPro
Dataset Card for CRMArena-Pro
Dataset Description
Paper Information
Citation
Dataset Description
CRMArena-Pro is a benchmark for evaluating LLM agents' ability to perform real-world work tasks in realistic environment. It expands on CRMArena with nineteen expert-validated tasks across sales, service, and "configure, price, and quote" (CPQ) processes, for both Business-to-Business (B2B) and Business-to-Customer (B2C) scenarios. CRMArena-Pro distinctively incorporates… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/CRMArenaPro.CRMArena
Dataset Card for CRMArena
Dataset Description
Paper Information
Citation
Dataset Description
CRMArena is a benchmark for evaluating LLM agents' ability to perform real-world work tasks in realistic environment. This benchmark is introduced in the paper "CRMArena: Understanding the Capacity of LLM Agents to Perform Professional CRM Tasks in Realistic Environments". We include 16 commonly-used industrial objects (e.g., account, order, knowledge article, case) with… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/CRMArena.Hard2Verify
Hard2Verify: A Step-Level Verification Benchmark for Open-Ended Frontier Math
Large language model (LLM)-based reasoning systems have recently achieved gold medal-level performance in the IMO 2025 competition, writing mathematical proofs where, to receive full credit, each step must be not only correct but also sufficiently supported. To train LLM-based reasoners in such challenging, open-ended settings, strong verifiers capable of catching step-level mistakes are necessary… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/Hard2Verify.SCOPE-Persona
SCOPE Personas (Nemotron Augmentation)
This dataset contains synthetic persona profiles constructed from socio-psychological framework (SCOPE) [https://arxiv.org/pdf/2601.07110], designed to better support LLM simulation usecases in social and behavioral science. It is intended to be used alongside Nemotron-Persona [https://huggingface.co/datasets/nvidia/Nemotron-Personas-USA]. Personas are grounded in a 141-item sociopsychological questionnaire spanning eight facets.
You can… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/SCOPE-Persona.summedits
Factual Consistency in Summarization
Can you tell which edits of summaries are consistent, and which are inconsistent?
SummEdits Benchmark (Section 6-7)
We release the 6,348 samples of data for the 10 domains in the SummEdits. Each sample has entries for:
domain: out of the 10 domains in SummEdits,
id: a unique ID for the sample,
doc: the input document,
summary: the summary that is either consistent or inconsistent with the facts in the document,
label: 1 if the… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/summedits.PROVE
Trust but Verify: Programmatic VLM Evaluation in the Wild
Viraj Prabhu, Senthil Purushwalkam, An Yan, Caiming Xiong, Ran Xu
Explorer
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Vision-Language Models (VLMs) often generate plausible but incorrect responses to visual queries. However, reliably quantifying the effect of such hallucinations in free-form responses to open-ended queries is challenging as it requires visually verifying each claim within the response. We propose Programmatic VLM… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/PROVE.Vietnamese-Salesforce-xlam-function-calling-60k-gg-translatedCogAlign
Dataset Card for CogAlign
Dataset Description
Citation
Dataset Description
CogAlign is a post-training strategy for Vision Language Models (VLMs) aimed at enhancing their visual arithmetic capabilities. This repository presents the training data for CogAlign, a synthetic dataset containing 64,000 examples designed to facilitate this post-training process.
CogAlign is inspired by Piaget's theory of cognitive development and focuses on improving a VLM's understanding of… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/CogAlign.vibepass
VIBEPASS: Can Vibe Coders Really Pass the Vibe Check?
Authors: Srijan Bansal, Jiao Fangkai, Yilun Zhou, Austin Xu, Shafiq Joty, Semih Yavuz
TL;DR: As LLMs shift programming toward human-guided "vibe coding", agentic tools increasingly rely on models to self-diagnose and repair their own subtle faults—a capability central to autonomous software engineering yet never systematically evaluated. VIBEPASS presents the first empirical benchmark that decomposes fault-targeted reasoning into… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/vibepass.Salesforce__LLaMA-3-8B-SFR-Iterative-DPO-R-details
Dataset Card for Evaluation run of Salesforce/LLaMA-3-8B-SFR-Iterative-DPO-R
Dataset automatically created during the evaluation run of model Salesforce/LLaMA-3-8B-SFR-Iterative-DPO-R
The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/Salesforce__LLaMA-3-8B-SFR-Iterative-DPO-R-details.shared-imagination
Dataset Card for Shared Imagination
This dataset contains the problems used in the paper Shared
Dataset Description
This dataset contains the questions generated for the investigations described in the TMLR paper Shared Imagination: LLMs Hallucinate Alike.
If you want to use this dataset to assess new models, please use the default config (i.e., datasets.load_dataset('Salesforce/shared-imagination')).
This config contains questions for which the four candidate choices… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/shared-imagination.InstruSum
InstruSum
This is the dataset corresponding to our paper "Benchmarking Generation and Evaluation Capabilities of Large Language
Models for Instruction Controllable Summarization".
dataset
The dataset subset contains 100 human-written data examples by us.
Each example contains an article, a summary instruction, a LLM-generated summary, and a hybrid LLM-human summary.
human_eval
This subset contains human evaluation results for the 100 examples in the dataset… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/InstruSum.summexecedit
Factual Consistency in Summarization
Evaluate your model's ability to detect and explain the factual inconsistency in summaries. This repo contains the benchmark from our paper "SummExecEdit: A Factual Consistency Benchmark in Summarization with Executable Edits".
SummExecEdit Benchmark
This benchmark is built over our previous benchmark - SummEdits. Consistent summaries are used from SummEdits. New inconsistent and challenging summaries are generated using executable… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/summexecedit.Vietnamese-Salesforce-xlam-function-calling-60k-gg-translatedSalesforce_LAMP-PSalesforce_LAMP-PRSalesforce_LAMP-PR-exp-predictSalesforce_LAMP-RSalesforce_LAMP-P-expSalesforce_LAMP-PR-expdealscope-salesforce-ai-brief-dataset-v1
DealScope Salesforce AI Brief Dataset v1
Dataset Summary
This dataset contains 25 structured Salesforce-record brief examples in the DealScope output format.
Each record is shaped like a real DealScope API response and includes:
record metadata
buying signals
risks
stakeholders
a draft follow-up email
a multi-line summary
The dataset is intended as a public retrieval and reference asset for Salesforce-focused AI brief workflows.
What Is In This Release
2… See the full description on the dataset page: https://huggingface.co/datasets/DealScopeAI/dealscope-salesforce-ai-brief-dataset-v1.Salesforce_LAMP-P-exp-predictsalesforce-apex-lwc-dataset
Dataset Card — SalesforceAI: Open Apex & LWC Fine-Tuning Dataset
Dataset Summary
SalesforceAI is the first large-scale, open fine-tuning dataset for Salesforce Apex and Lightning Web Components (LWC). It contains thousands of instruction-following prompt-response pairs covering real Salesforce development scenarios, validated by a certified Salesforce developer with hands-on platform experience.
Supported Tasks
Code generation (Apex, LWC, SOQL)… See the full description on the dataset page: https://huggingface.co/datasets/RahulShettyRavipudi/salesforce-apex-lwc-dataset.salesforce_codesmellssales_force_dataset1