CRM or Customer Relationship Management is a system that allows businesses to manage contacts, collaborate effectively, increase productivity & grow your business. Traditionally Customer Relationship Management has been a sales tool but now CRM systems, like Salesforce, allow people to do more.

Dataset

This dataset was created by Ashish Pandey

Contents

Dataset Card for "wikitext"

  Dataset Summary

The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified Good and Featured articles on Wikipedia. The dataset is available under the Creative Commons Attribution-ShareAlike License. Compared to the preprocessed version of Penn Treebank (PTB), WikiText-2 is over 2 times larger and WikiText-103 is over 110 times larger. The WikiText dataset also features a far… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/wikitext.

Dataset

This dataset was created by sayoulala

Contents

This dataset contains service requests received from external customers. This data is collected through the council's Salesforce system. It contains information about the service type, status, and dates for received and closed. This dataset only contains a subset of the received requests as it is extracted from one of several customer requests systems.

Results of factor loadings, Cronbach’s alpha, CR, and AVE.

🌮 TACO: Learning Multi-modal Action Models with Synthetic Chains-of-Thought-and-Action

🌐 Website | 📑 Arxiv | 💻 Code| 🤗 Datasets

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  Summary

TLDR: CoTA is a large-scale dataset of synthetic Chains-of-Thought-and-Action (CoTA) generated by programs.

  Load data

from datasets import load_dataset dataset = load_dataset("Salesforce/program-cota-llava"… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/program-cota-llava.

Results of PLS predict test.

Post-Abatement Verification reports for residential and commercial properties for the City of Detroit's demolition program. Post-Abatement Verification reports are pulled from the Salesforce CRM system.As of July 29, 2024, this dataset is deprecated and is no longer being updated. Please find the current authoritative version of this dataset here: https://data.detroitmi.gov/datasets/c0f1059e3f9345dea13e1a7fe8a8ecc0_0/about

Dataset

This dataset was created by TommyD

Contents

This dataset is about books and is filtered where the book is Motivating your sales force, featuring 7 columns including author, BNB id, book, book publisher, and ISBN. The preview is ordered by publication date (descending).

Post-Abatement Verification reports for residential and commercial properties for the City of Detroit's demolition program. Post-Abatement Verification reports are pulled from the Salesforce CRM system.As of July 29, 2024, this dataset is deprecated and is no longer being updated. Please find the current authoritative version of this dataset here: https://data.detroitmi.gov/datasets/c0f1059e3f9345dea13e1a7fe8a8ecc0_0/about

🥬 BLIP3-KALE:Knowledge Augmented Large-scale Dense Captions

BLIP3-KALE is an open-source dataset of 218 million image-text pairs, featuring knowledge-augmented dense captions combining web-scale knowledge with detailed image descriptions.

Paper: [To be added]

  Uses

BLIP3-KALE is designed to facilitate research in multimodal pretraining. The dataset can be used for training large multimodal models that require factually grounded, dense image captions. It has already been an… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/blip3-kale.

FaithEval

FaithEval is a new and comprehensive benchmark dedicated to evaluating contextual faithfulness in LLMs across three diverse tasks: unanswerable, inconsistent, and counterfactual contexts. [Paper] FaithEval: Can Your Language Model Stay Faithful to Context, Even If "The Moon is Made of Marshmallows", ICLR 2025, https://arxiv.org/abs/2410.03727 [Code and Detailed Instructions] https://github.com/SalesforceAIResearch/FaithEval

  Disclaimer and Ethical Considerations… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/FaithEval-counterfactual-v1.0.

This dataset is about books and is filtered where the book is Contemporary sales force management, featuring 7 columns including author, BNB id, book, book publisher, and ISBN. The preview is ordered by publication date (descending).

This map layer contains the shallowest principal aquifers of the conterminous United States, Hawaii, Puerto Rico, and the U.S. Virgin Islands, portrayed as polygons. The map layer was developed as part of the effort to produce the maps published at 1:2,500,000 in the printed series "Ground Water Atlas of the United States". The published maps contain base and cultural features not included in these data. This is a replacement for the July 1998 map layer called Principal Aquifers of the 48 Conterminous United States.

The dataset/corpus is supplementary material for the paper titled "The dataset corpus is supplementary material for the paper "Exploring mobile application user experience through topic modeling" by Olivera Grljević, Mirjana Marić, and Rade Božić.This paper is focused on identifying factors influencing satisfaction and dissatisfaction with the SaleForce mobile application, which impact the user experience and consequently loyalty. Since online reviews reflect positive, negative, or neutral opinions, attitudes, and sentiments towards a certain entity [26], we restricted our research to online reviews of the SalesForce application. The data is collected from the Google Play Store[1] using a custom-written Python code for scraping the websites’ content.Corpus contains 9.296 online reviews of the mobile application, after addressing multilingualism in data by translating it to English.When using dataset, please use the following reference:Grljević, O., Marić, M., & Božić, R. (2025). Exploring Mobile Application User Experience Through Topic Modeling. Sustainability, 17(3), 1109. https://doi.org/10.3390/su17031109[1] The URL location of SalesForce mobile application on Google Play Store: https://play.google.com/store/apps/details?id=com.salesforce.chatter&hl=en&gl=US

The Commercial Demolitions program is managed by the Construction and Demolition Department (CDD) and was established in 2014 to address the unique circumstances of commercial demolitions. Before entering the demolition pipeline, structures are selected by the Detroit Land Bank Authority (DLBA) or get approval from the Detroit City Council. Structures identified for commercial demolition include vacant retail, industrial, and multifamily (4 unit and above) residential properties deemed unsuitable for stabilization and rehabilitation. This dataset provides information about structures as they move through the demolition process. It includes where the structures are located, along with key dates, costs, and the different stages of the demolition process. Data is pulled directly from the City of Detroit’s Salesforce CRM (Customer Relationship Management) system daily and include structures that are in the demolition pipeline, under contract for demolition, and that have been demolished.

The goals of the commercial demolition program are to contribute to the health, environment, and beauty of Detroit by tackling commercial blight and preparing previously blighted properties for new uses and investment. Beginning in 2022, the main source of funding for the commercial demolition program has been the ARPA (American Rescue Plan Act).

Does Context Matter? ContextualJudgeBench for Evaluating LLM-based Judges in Contextual Settings

Austin Xu*, Srijan Bansal*, Yifei Ming, Semih Yavuz, Shafiq Joty (* = co-lead, equal contribution) TL;DR: ContextualJudgeBench is a pairwise benchmark with 2,000 samples for evaluating LLM-as-judge models in two contextual settings: Contextual QA and summarization. We propose a pairwise evaluation hierarchy and generate splits for our proposed hierarchy. To run evaluation on… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/ContextualJudgeBench.

DART is a large dataset for open-domain structured data record to text generation. DART consists of 82,191 examples across different domains with each input being a semantic RDF triple set derived from data records in tables and the tree ontology of the schema, annotated with sentence descriptions that cover all facts in the triple set.