datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
wikitext
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.GiftEvalPretrain
GIFT-Eval Pre-training Datasets
Pretraining dataset aligned with GIFT-Eval that has 71 univariate and 17 multivariate datasets, spanning seven domains and 13 frequencies, totaling 4.5 million time series and 230 billion data points. Notably this collection of data has no leakage issue with the train/test split and can be used to pretrain foundation models that can be fairly evaluated on GIFT-Eval.
📄 Paper
🖥️ Code
📔 Blog Post
🏎️ Leader Board
Ethical Considerations… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/GiftEvalPretrain.lotsa_data
LOTSA Data
The Large-scale Open Time Series Archive (LOTSA) is a collection of open time series datasets for time series forecasting.
It was collected for the purpose of pre-training Large Time Series Models.
See the paper and codebase for more information.
Citation
If you're using LOTSA data in your research or applications, please cite it using this BibTeX:
BibTeX:
@article{woo2024unified,
title={Unified Training of Universal Time Series Forecasting Transformers}… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/lotsa_data.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.3d_optical_flow_droid
3D Optical Flow DROID Dataset
Processed DROID robotics dataset with optical flow and scene flow annotations.
Dataset Structure
Organized by lab, each trajectory in separate tar.gz archive:
IPRL/IPRL+2023-06-19+Mon_Jun_19_23:27:48_2023.tar.gz
CLVR/CLVR+2023-...tar.gz
... (15 labs, ~33K trajectories)
Each trajectory contains:
metadata.json - Trajectory metadata
trajectory.h5 - Robot state and actions
camera_left/, camera_right/ - Camera data
rgb/ - RGB images
depth/ -… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/3d_optical_flow_droid.blip3-kale
🥬 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.cos_e
Dataset Card for "cos_e"
Dataset Summary
Common Sense Explanations (CoS-E) allows for training language models to
automatically generate explanations that can be used during training and
inference in a novel Commonsense Auto-Generated Explanation (CAGE) framework.
Supported Tasks and Leaderboards
More Information Needed
Languages
More Information Needed
Dataset Structure
Data Instances
v1.0
Size of downloaded dataset… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/cos_e.GiftEval
GIFT-Eval
We present GIFT-Eval, a benchmark designed to advance zero-shot time series forecasting by facilitating evaluation across diverse datasets. GIFT-Eval includes 23 datasets covering 144,000 time series and 177 million data points, with data spanning seven domains, 10 frequencies, and a range of forecast lengths. This benchmark aims to set a new standard, guiding future innovations in time series foundation models.
To facilitate the effective pretraining and evaluation… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/GiftEval.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.self-improve-fragilityUniDoc-Bench
UNIDOC-BENCH Dataset
A unified benchmark for document-centric multimodal retrieval-augmented generation (MM-RAG).
Dataset Description
UNIDOC-BENCH is the first large-scale, realistic benchmark for multimodal retrieval-augmented generation (MM-RAG) and Visual Question Answering (VQA) built from 70,000 real-world PDF pages across eight domains. The dataset extracts and links evidence from text, tables, and figures, then generates 1,700+ multimodal QA pairs spanning… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/UniDoc-Bench.ConvoMem
Conversational Memory Benchmark
A comprehensive benchmark for evaluating conversational memory in large language models, featuring 75,336 question-answer pairs across six evidence categories. This benchmark addresses the critical challenge of memory management in conversational AI systems, where models must retain, update, and utilize information across extended multi-turn dialogues.
📚 Resources
Paper: ConvoMem Benchmark: Why Your First 150 Conversations Don't Need RAG… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/ConvoMem.Salesforce-xlam-function-calling-60kblip3-ocr-200m
BLIP3-OCR-200M Dataset
Overview
The BLIP3-OCR-200M dataset is designed to address the limitations of current Vision-Language Models (VLMs) in processing and interpreting text-rich images, such as documents and charts. Traditional image-text datasets often struggle to capture nuanced textual information, which is crucial for tasks requiring complex text comprehension and reasoning.
Key Features
OCR Integration: The dataset incorporates Optical Character… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/blip3-ocr-200m.BLIP3o-NEXT-EDIT-ENSEMBLE-DATASETSCRMArenaPro
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.LiveResearchBench
Dataset Overview
LiveResearchBench provides expert-curated, real-world tasks spanning daily life, enterprise, and academia, each requiring extensive, real-time web search, multi-source reasoning, and cross-domain synthesis. DeepEval offers human-aligned protocols for reliable, systematic evaluation of agentic systems on open-ended deep research tasks.
📌 Quick Links
Project Page
Paper
Codebase
Dataset Fields
Subsets:
question_with_checklist: Full dataset with… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/LiveResearchBench.wikisqlA large crowd-sourced dataset for developing natural language interfaces for relational databasesFinTrain
💰 Demystifying Domain-adaptive Post-training for Financial LLMs
This is the training data used in the recipe described in our paper:📄 Demystifying Domain-adaptive Post-training for Financial LLMs
For more details, please check the following resources:
🌐 Project Page: https://vincent950129.github.io/adapt-llm/
📚 Trained Model: https://huggingface.co/Salesforce/Llama-Fin-8b
🧠 Evaluation Data: https://huggingface.co/datasets/Salesforce/FinEval
💻 Code Repository:… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/FinTrain.cloudops_tsf
Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain
Paper | Code
Datasets accompanying the paper "Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain".
Quick Start
pip install datasets==2.12.0 fsspec==2023.5.0
azure_vm_traces_2017
from datasets import load_dataset
dataset = load_dataset('Salesforce/cloudops_tsf', 'azure_vm_traces_2017')
print(dataset)
DatasetDict({
train_test: Dataset({… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/cloudops_tsf.MTA-Vision-DeepSearchCRMArena
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.FaithEval-counterfactual-v1.0
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.Salesforce-xlam-function-calling-60kfineweb_deduplicated
TL;DR
Fineweb is a popular and high quality open dataset. This dataset is a deduplicated version of Fineweb - removing rows with duplicate text, collecting counts.
Motivation
Fineweb is an open text dataset intended for training language models. It's one of the highest quality and most popular open datasets available. It has been produced by a reputable AI lab - HuggingFace and has been downloaded tens of thousands of times.
Fineweb dataset is 93.4 TB and has 15T… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/fineweb_deduplicated.FaithEval-unanswerable-v1.0
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-unanswerable-v1.0.FaithEval-inconsistent-v1.0
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-inconsistent-v1.0.details_Salesforce__codegen-16B-nl
Dataset Card for Evaluation run of Salesforce/codegen-16B-nl
Dataset Summary
Dataset automatically created during the evaluation run of model Salesforce/codegen-16B-nl on the Open LLM Leaderboard.
The dataset is composed of 122 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 3 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… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard-old/details_Salesforce__codegen-16B-nl.ContextualBench
ContextualBench - A comprehensive toolkit to evaluate LM on different Contextual datasets
Evaluation Code: SalesforceAIResearch/SFR-RAG
Description
ContextualBench is a powerful evaluation framework designed to assess the performance of Large Language Models (LLMs) on contextual datasets. It provides a flexible pipeline for evaluating various LLM families across different tasks, with a focus on handling large context inputs.
Users need to make their own assessment… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/ContextualBench.cota-mantis
🌮 TACO: Learning Multi-modal Action Models with Synthetic Chains-of-Thought-and-Action
🌐 Website | 📑 Arxiv | 💻 Code| 🤗 Datasets
If you like our project or are interested in its updates, please star us :) Thank you! ⭐
Summary
TLDR: CoTA is a large-scale dataset of synthetic Chains-of-Thought-and-Action (CoTA) generated by multi-modal large language models.
Load data
from datasets import load_dataset
dataset =… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/cota-mantis.