datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
amazon_massive_scenario
MassiveScenarioClassification
An MTEB dataset
Massive Text Embedding Benchmark
MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages
Task category
t2c
Domains
Spoken
Reference
https://arxiv.org/abs/2204.08582
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_tasks(["MassiveScenarioClassification"])
evaluator =… See the full description on the dataset page: https://huggingface.co/datasets/mteb/amazon_massive_scenario.bold
Dataset Card for Bias in Open-ended Language Generation Dataset (BOLD)
Dataset Description
Bias in Open-ended Language Generation Dataset (BOLD) is a dataset to evaluate fairness in open-ended language generation in English language. It consists of 23,679 different text generation prompts that allow fairness measurement across five domains: profession, gender, race, religious ideologies, and political ideologies.
Some examples of prompts in BOLD are as follows:
Many… See the full description on the dataset page: https://huggingface.co/datasets/AmazonScience/bold.amazon_counterfactual_en
Amazon Counterfactual Statements
This dataset is the en-ext split from SetFit/amazon_counterfactual. As the original test set is rather small (1333 examples), a different split was created with 50-50 for training & testing.
The dataset is described in amazon-multilingual-counterfactual-dataset / Paper
It contains statements from Amazon reviews about events that did not or cannot take place.
amazon_reviews_multi_enSIFT-50M
Dataset Card for SIFT-50M
SIFT-50M (Speech Instruction Fine-Tuning) is a 50-million-example dataset designed for instruction fine-tuning and pre-training of speech-text large language models (LLMs). It is built from publicly available speech corpora containing a total of 14K hours of speech and leverages LLMs and off-the-shelf expert models. The dataset spans five languages, covering diverse aspects of speech understanding and controllable speech generation instructions. SIFT-50M… See the full description on the dataset page: https://huggingface.co/datasets/amazon-agi/SIFT-50M.amazon-benchmark
Amazon query–bundle benchmark
Canonical, category-organized query and reference-positive data. Experiment traces should reference this repository by commit SHA, category, split, and candidate_id, rather than republishing the dataset.
Musical Instruments
Split
Examples
agent_dev
2,028
agent_hidden
1,960
Each record contains a query and 3–7 reference product IDs. These are observed reference positives, not exhaustive labels for all valid… See the full description on the dataset page: https://huggingface.co/datasets/iaouali/amazon-benchmark.amazon_massive_intent_en-USAutoSUIT
AutoSUIT Bench (HuggingFace edition)
Dynamic, execution-based benchmark for secure code generation by LLMs. Every generated
program is compiled/interpreted and run against two independent unit-test suites — a
functional suite and a security suite (the latter is designed to fail when the target
CWE vulnerability is present). Covers 232 CWEs across C, C++, Java, and Python.
Paper: Osebe et al., AutoSUIT Bench — Automated Security UnIt Test Benchmark for LLM
Coding, Findings of… See the full description on the dataset page: https://huggingface.co/datasets/amazon/AutoSUIT.FalseReject
FalseReject: A Dataset for Over-Refusal Mitigation in Large Language Models
FalseReject is a large-scale dataset designed to mitigate over-refusal behavior in large language models (LLMs)—the tendency to reject safe prompts that merely appear sensitive. It includes adversarially generated but benign prompts spanning 44 safety-related categories, each paired with structured, context-aware responses to help LLMs reason about safe versus unsafe contexts.
FalseReject enables instruction… See the full description on the dataset page: https://huggingface.co/datasets/AmazonScience/FalseReject.amazon_reviews_multi_ja#amazon reviews multi japanese
This dataset is a port of the official ['amazon_reviews_multi' dataset] (https://huggingface.co/datasets/amazon_reviews_multi) on the Hub. It has just the Japanese language version. It has been reduced to just 3 columns (and 4th "label_text") that are relevant to the SetFit task.
DocTalk
📁 DocTalk: Scalable Graph-based Dialogue Synthesis for Enhancing LLM Conversational Capabilities
➤ 📖 Paper Link
DocTalk is a large-scale, synthetic dialogue corpus created via a three-stage pipeline that converts clusters of related Wikipedia documents into multi-turn, multi-topic information-seeking conversations.
The pipeline comprises:
Document Graph Construction: Sampling up to three related Wikipedia articles per anchor document via a weighted random walk on a directed… See the full description on the dataset page: https://huggingface.co/datasets/AmazonScience/DocTalk.amazon_reviews_multi
Amazon Reviews Multi (Data Files Version)
This dataset hosts the multilingual Amazon Reviews corpus as raw jsonl.gz data files for direct loading via datasets without using a dataset script.
Source
Original dataset name: amazon_reviews_multi
Original dataset card: https://huggingface.co/datasets/amazon_reviews_multi
Mirror used for raw file retrieval: https://huggingface.co/datasets/buruzaemon/amazon_reviews_multi
Features
Each record contains:
review_id… See the full description on the dataset page: https://huggingface.co/datasets/goosmanlei/amazon_reviews_multi.amazon_polarityamazontydi-as2
TyDi-AS2
Dataset Summary
TyDi-AS2 and Xtr-TyDi-AS2 are multilingual Answer Sentence Selection (AS2) datasets comprising 8 diverse languages, proposed in our paper accepted at ACL 2023 (Findings): Cross-Lingual Knowledge Distillation for Answer Sentence Selection in Low-Resource Languages.
Both the datasets were created from TyDi-QA, a multilingual question-answering dataset. TyDi-AS2 was created by converting the QA instances in TyDi-QA to AS2 instances (see Dataset… See the full description on the dataset page: https://huggingface.co/datasets/AmazonScience/tydi-as2.amazon_massive_intent_zh-CNamazon_massive_intent_de-DEamazon_reviews_multi_es#amazon reviews multi spanish
This dataset is a port of the official ['amazon_reviews_multi' dataset] (https://huggingface.co/datasets/amazon_reviews_multi) on the Hub. It has just the Spanish language version. It has been reduced to just 3 columns (and 4th "label_text") that are relevant to the SetFit task.
amazon_massive_scenario_en-USamazon_reviews_multi_de#amazon reviews multi german
This dataset is a port of the official ['amazon_reviews_multi' dataset] (https://huggingface.co/datasets/amazon_reviews_multi) on the Hub. It has just the German language version. It has been reduced to just 3 columns (and 4th "label_text") that are relevant to the SetFit task.
amazon_massive_intent_am-ETamazon_massive_intent_hi-INAmazonESCIAmazon ESCI is a large-scale test collection for product search, comprising: (i) a corpus of 1.2M products of the english portion of Amazon ESCI (ii) a set of queries with 74.9k, 13.5k, and 8.9k queries in the training, validation, and test sets, respectively. The relevance judgements comprise 20.15 assessments per query on average, divided in four relevance labels:
Exact, Substitute, Complementary, and Irrelevant, with gains of 1.0, 0.1, 0.01, and 0.0 associated. Only Exact products are… See the full description on the dataset page: https://huggingface.co/datasets/J-MADRAL/AmazonESCI.amazon_reviews_multi_zh#amazon reviews multi chinese
This dataset is a port of the official ['amazon_reviews_multi' dataset] (https://huggingface.co/datasets/amazon_reviews_multi) on the Hub. It has just the Chinese language version. It has been reduced to just 3 columns (and 4th "label_text") that are relevant to the SetFit task.
amazon_massive_intent_ja-JPamazon_massive_intent_ar-SAamazon_massive_intent_sw-KEamazon-2023-thai-1m
Amazon 2023 Thai 1M / ชุดข้อมูลสินค้า Amazon ภาษาไทย 1 ล้านรายการ
ไทย | English below
ชุดข้อมูลสินค้าอีคอมเมิร์ซภาษาไทย 1,000,000 รายการ แปลจากชุดข้อมูล Amazon Reviews '23 Extension ของ Google ด้วยโมเดล typhoon-translate-4b เพื่อใช้ทดสอบ/สาธิตระบบค้นหาเชิงความหมาย (semantic search) ภาษาไทย
แหล่งที่มา / Source & Attribution ⚠️ สำคัญ
ชุดข้อมูลนี้เป็นงานแปล (derivative work) จาก:
ชุดข้อมูลต้นทาง
google/extended_amazon_2023_dataset (Amazon Reviews '23… See the full description on the dataset page: https://huggingface.co/datasets/pnpkke/amazon-2023-thai-1m.amazon_reviews_multi_fr#amazon reviews multi french
This dataset is a port of the official ['amazon_reviews_multi' dataset] (https://huggingface.co/datasets/amazon_reviews_multi) on the Hub. It has just the French language version. It has been reduced to just 3 columns (and 4th "label_text") that are relevant to the SetFit task.
amazon_massive_intent_th-TH