Dataset

This dataset was created by RAHUL DHANOLA

Released under Apache 2.0

Contents

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/Salesforce/xlam-function-calling-60k.

This dataset is based on the Sample Leads Dataset and is intended to allow some simple filtering by lead source. I had modified this dataset to support an upcoming Towards Data Science article walking through the process. Link to be shared once published.

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.

Factori houses an extensive dataset of US People data, providing valuable insights into individuals across various demographic and behavioral dimensions. Our US People Data section is dedicated to helping you understand the breadth and depth of the information available through our API.

Data Collection and Aggregation Our People data is gathered and aggregated through surveys, digital services, and public data sources. We use powerful profiling algorithms to collect and ingest only fresh and reliable data points. This ensures that the data you access is up-to-date and accurate.

Here are some of the data categories and attributes we offer within US People data Graph: - Geography: City, State, ZIP, County, CBSA, Census Tract, etc. - Demographics: Gender, Age Group, Marital Status, Language, etc. - Financial: Income Range, Credit Rating Range, Credit Type, Net Worth Range, etc. - Persona: Consumer type, Communication preferences, Family type, etc. - Interests: Content, Brands, Shopping, Hobbies, Lifestyle, etc. - Household: Number of Children, Number of Adults, IP Address, etc. - Behaviors: Brand Affinity, App Usage, Web Browsing, etc. - Firmographics: Industry, Company, Occupation, Revenue, etc. - Retail Purchase: Store, Category, Brand, SKU, Quantity, Price, etc.

Here's the data schema: Person_id first_name last_name gender age year month day full_address city state zipcode zip4 delivery_point_bar_code carrier_route walk_sequence_code fips_state_code fips_county_code country_name latitude longtitude address_type metropolitan_statistical_area core_based_statistical_area census_tract census_block
census_block_group primary_address pre_address street post_address address_suffix address_secondline address_abrev
census_median_home_value home_market_value property_build_year property_with_ac property_with_pool property_with_water property_with_sewer general_home_value property_fuel_type household_id census_median_household_income
household_size occupation_home_office
dwell_type household_income marital_status length_of_residence number_of_kids pre_school_kids single_parent working_women_in_house_hold homeowner children
adults generations net_worth education_level education_history occupation occuptation_business_owner credit_lines credit_card_user newly_issued_credit_card_user credit_range_new credit_cards loan_to_value and alot more...

Salesforce Backup Market Outlook

According to our latest research, the global Salesforce Backup market size reached USD 1.56 billion in 2024, reflecting robust demand across multiple verticals. The market is set to maintain a strong upward trajectory with a projected CAGR of 18.7% from 2025 to 2033, ultimately reaching a forecasted value of USD 7.58 billion by 2033. This remarkable growth is primarily driven by the increasing adoption of cloud-based CRM solutions, the growing awareness of data loss risks, and stringent compliance requirements across industries.

One of the most significant growth factors propelling the Salesforce Backup market is the exponential rise in data volumes generated by organizations leveraging Salesforce as their primary customer relationship management platform. As businesses increasingly rely on Salesforce for mission-critical operations, the risk associated with data loss, corruption, or cyberattacks has become a paramount concern. This has led to a surge in demand for robust backup and recovery solutions tailored specifically for Salesforce environments. Enterprises are now prioritizing data resilience and business continuity, recognizing that accidental deletions, malicious activities, or system failures can result in substantial financial and reputational losses. Consequently, vendors are innovating with advanced backup technologies, including automated scheduling, granular recovery, and seamless integration, to address these evolving needs.

Another key driver for the Salesforce Backup market is the tightening regulatory landscape. Organizations across sectors such as BFSI, healthcare, and government are increasingly subject to stringent data protection laws, including GDPR, HIPAA, and CCPA. These regulations mandate not only the safeguarding of sensitive data but also the ability to restore information quickly in the event of loss or breach. As a result, compliance-driven adoption of Salesforce backup solutions is on the rise, with businesses seeking tools that provide audit trails, encryption, and comprehensive reporting capabilities. The emphasis on regulatory compliance is compelling IT decision-makers to invest in third-party backup services that go beyond Salesforce’s native data protection offerings, ensuring both legal adherence and operational resilience.

The rapid shift toward remote and hybrid work models in the post-pandemic era has also significantly influenced the Salesforce Backup market. With employees accessing Salesforce from multiple locations and devices, the risks of inadvertent data loss or unauthorized access have escalated. This has underscored the importance of robust backup strategies that can accommodate distributed workforces and dynamic data environments. Cloud-based backup solutions, in particular, have gained traction due to their scalability, ease of deployment, and ability to provide real-time protection regardless of user location. Vendors are responding by enhancing their offerings with features such as continuous backup, disaster recovery, and instant restore, ensuring that organizations can maintain seamless operations in the face of unforeseen disruptions.

From a regional perspective, North America continues to dominate the Salesforce Backup market, driven by the high concentration of Salesforce users, advanced IT infrastructure, and a strong focus on data governance. However, Asia Pacific is emerging as a lucrative growth region, fueled by rapid digital transformation, expanding cloud adoption, and increasing awareness of data security best practices. Europe, with its rigorous data protection regulations, also represents a significant market, while Latin America and the Middle East & Africa are witnessing steady adoption as organizations modernize their IT ecosystems. The global nature of Salesforce deployments ensures that demand for backup solutions is rising across all geographies, with vendors tailoring their offerings to meet region-specific compliance and operational requirements.

Component Analysis

Description:

This dataset contains information about customer interactions, sales activities, and opportunities from a fictional company's CRM (Customer Relationship Management) system. The dataset is designed to help data scientists and analytics understand the sales process, identify trends and patterns, and build predictive models to improve sales performance.

Features:

• Customer information (demographics, firmographics, etc.)
• Sales activities
• Opportunity data (deal size, stage, probability, etc.)
• Product/service information
• Sales team and performance metrics
• Time-series data (daily/weekly/monthly sales, etc.)

Use Cases:

• Predicting won/lost opportunities
• Forecasting deal size
• Identifying key drivers of sales performance
• Optimizing sales team performance

• Analyzing customer behavior and preferences

  This dataset is perfect for data scientists, analysts, and students looking to practice their skills in:

• Predictive modeling

• Data visualization

• Sales analytics

• Customer relationship management

Get started: Download the dataset and start exploring!

Grounding Dataset

A comprehensive, high-quality dataset for GUI element grounding tasks, curated from multiple authoritative sources to provide diverse, well-annotated interface interactions.

  Overview

This dataset combines and standardizes annotations from five major GUI interaction datasets:

Aria-UI OmniAct Widget Caption UI-Vision OS-Atlas

  Dataset Schema

Each sample contains the following fields:

Field Type Description Example

dataset string… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/grounding_dataset.

Auto-generated structured data of Salesforce Field Reference from table Fields

According to Cognitive Market Research, the global Data Integration Market size was USD 15.24 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 12.31% from 2024 to 2031. Key Dynamics of

Data Integration Market

Key Drivers of

Data Integration Market

Explosion of Data Across Disparate Systems: Organizations are producing enormous quantities of data across various platforms such as CRMs, ERPs, IoT devices, social media, and third-party services. Data integration tools facilitate unified access, allowing businesses to obtain comprehensive insights by merging both structured and unstructured data—thereby enhancing analytics, reporting, and operational decision-making.

Demand for Real-Time Business Intelligence: Contemporary enterprises necessitate real-time insights to maintain their competitive edge. Real-time data integration enables the smooth synchronization of streaming and batch data from diverse sources, fostering dynamic dashboards, tailored user experiences, and prompt reactions to market fluctuations or operational interruptions.

Adoption of Hybrid and Multi-Cloud Environments: As organizations embrace a combination of on-premise and cloud applications, the integration of data across these environments becomes essential. Data integration solutions guarantee seamless interoperability, facilitating uninterrupted data flow across platforms such as Salesforce, AWS, Azure, SAP, and others—thereby removing silos and promoting collaboration.

Key Restraints for

Data Integration Market

Complexity of Legacy Systems and Data Silos: Many organizations continue to utilize legacy databases and software that operate with incompatible formats. The integration of these systems with contemporary cloud tools necessitates extensive customization and migration strategies—rendering the process laborious, prone to errors, and demanding in terms of resources.

Data Governance and Compliance Challenges: Achieving secure and compliant data integration across various borders and industries presents significant challenges. Regulations such as GDPR, HIPAA, and CCPA impose stringent requirements on data management, thereby heightening the complexity of system integration without infringing on privacy or compromising sensitive information.

High Cost and Technical Expertise Requirements: Implementing enterprise-level data integration platforms frequently demands considerable financial investment and the expertise of skilled professionals for ETL development, API management, and error resolution. Small and medium-sized enterprises may perceive the financial and talent demands as obstacles to successful adoption.

Key Trends in

Data Integration Market

The Emergence of Low-Code and No-Code Integration Platforms: Low-code platforms are making data integration accessible to non-technical users, allowing them to design workflows and link systems using intuitive drag-and-drop interfaces. This movement enhances time-to-value and lessens reliance on IT departments—making it particularly suitable for agile, fast-growing companies.

AI-Driven Automation for Data Mapping and Transformation: Modern platforms are increasingly utilizing machine learning to automatically identify schemas, propose transformation rules, and rectify anomalies. This minimizes manual labor, improves data quality, and accelerates integration processes—facilitating more effective data pipelines for analytics and artificial intelligence.

Heightened Emphasis on Data Virtualization and Federation: Instead of physically transferring or duplicating data, organizations are embracing data virtualization. This strategy enables users to access and query data from various sources in real time, without the need for additional storage—enhancing agility and lowering storage expenses. Introduction of the Data Integration Market Market

Data Integration Market is the increasing need for seamless access and analysis of diverse data sources to support informed decision-making and digital transformation initiatives. As organizations accumulate vast amounts of data from various systems, applications, and platforms, integrating this data into a unified view becomes crucial. Data integration solutions enable businesses to break down data silos, ensuring consistent, accurate, and real-time data availability acr...

J4R: Learning to Judge with Equivalent Initial State Group Relative Policy Optimization

Austin Xu, Yilun Zhou, Xuan-Phi Nguyen, Caiming Xiong, Shafiq Joty To run evaluation, please see our Github repo.

💻 Github: https://github.com/SalesforceAIResearch/ReasoningJudgeBench 📜 Paper: https://arxiv.org/abs/2505.13346

  ReasoningJudgeBench

ReasoningJudgeBench is a 1,483 sample pairwise benchmark introduced in the paper J4R: Learning to Judge with Equivalent Initial State… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/ReasoningJudgeBench.

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A large crowd-sourced dataset for developing natural language interfaces for relational databases

DialogStudio: Unified Dialog Datasets and Instruction-Aware Models for Conversational AI

Author: Jianguo Zhang, Kun Qian Paper|Github|[GDrive] 🎉 March 18, 2024: Update for AI Agent. Check xLAM for the latest data and models relevant to AI Agent! 🎉 March 10 2024: Update for dataset viewer issues:

Please refer to https://github.com/salesforce/DialogStudio for view of each dataset, where we provide 5 converted examples along with 5 original examples under each data folder. For… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/dialogstudio.

BLIP3-GROUNDING-50M Dataset

  Overview

The BLIP3-GROUNDING-50M dataset is designed to enhance the ability of Vision-Language Models (VLMs) to ground semantic concepts in visual features, which is crucial for tasks like object detection, semantic segmentation, and understanding referring expressions (e.g., "the object to the left of the dog"). Traditional datasets often lack the necessary granularity for such tasks, making it challenging for models to accurately localize and… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/blip3-grounding-50m.