datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
or-bench
OR-Bench: An Over-Refusal Benchmark for Large Language Models
Please see our demo at HuggingFace Spaces.
Overall Plots of Model Performances
Below is the overall model performance. X axis shows the rejection rate on OR-Bench-Hard-1K and Y axis shows the rejection rate on OR-Bench-Toxic. The best aligned model should be on the top left corner of the plot where the model rejects the most number of toxic prompts and least number of safe prompts. We also plot a blue line… See the full description on the dataset page: https://huggingface.co/datasets/bench-llm/or-bench.Bitext-customer-support-llm-chatbot-training-dataset
Bitext - Customer Service Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the Customer Support sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-customer-support-llm-chatbot-training-dataset.Bitext-retail-ecommerce-llm-chatbot-training-dataset
Bitext - Retail (eCommerce) Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [Retail (eCommerce)] sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-retail-ecommerce-llm-chatbot-training-dataset.Bitext-events-ticketing-llm-chatbot-training-dataset
Bitext - Events and Ticketing Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [events and ticketing] sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-events-ticketing-llm-chatbot-training-dataset.or-bench
OR-Bench: An Over-Refusal Benchmark for Large Language Models
Please see our demo at HuggingFace Spaces.
Overall Plots of Model Performances
Below is the overall model performance. X axis shows the rejection rate on OR-Bench-Hard-1K and Y axis shows the rejection rate on OR-Bench-Toxic. The best aligned model should be on the top left corner of the plot where the model rejects the most number of toxic prompts and least number of safe prompts. We also plot a blue line… See the full description on the dataset page: https://huggingface.co/datasets/bench-llms/or-bench.or-bench
OR-Bench: An Over-Refusal Benchmark for Large Language Models
Please see our leaderboard at HuggingFace Spaces.
Overall Plots of Model Performances
Below is the overall model performance. X axis shows the rejection rate on OR-Bench-Hard-1K and Y axis shows the rejection rate on OR-Bench-Toxic. The best aligned model should be on the top left corner of the plot where the model rejects the most number of toxic prompts and least number of safe prompts. We also plot a blue… See the full description on the dataset page: https://huggingface.co/datasets/orbench-llm/or-bench.or-bench-toxic-all
OR-Bench: An Over-Refusal Benchmark for Large Language Models
This dataset constains highly toxic prompts, use with caution!!!
Please see our demo at HuggingFace Spaces.
Overall Plots of Model Performances
Below is the overall model performance. X axis shows the rejection rate on OR-Bench-Hard-1K and Y axis shows the rejection rate on OR-Bench-Toxic. The best aligned model should be on the top left corner of the plot where the model rejects the most number of toxic… See the full description on the dataset page: https://huggingface.co/datasets/bench-llms/or-bench-toxic-all.Bitext-telco-llm-chatbot-training-dataset
Bitext - Telco Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [telco] sector can be easily achieved using our two-step approach to LLM Fine-Tuning. An overview of… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-telco-llm-chatbot-training-dataset.Bitext-insurance-llm-chatbot-training-dataset
Bitext - Insurance Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [insurance] sector can be easily achieved using our two-step approach to LLM Fine-Tuning. An… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-insurance-llm-chatbot-training-dataset.llm-jp-longbench-JEMHop
llm-jp-longbench-JEMHopQA
llm-jp LongBench ベンチマークについて
このデータセットは，GitHub リポジトリhttps://github.com/llm-jp/llm-jp-longbenchで公開されているllm-jp LongBenchベンチマークの評価対象データセットの一部として構築されています。
llm-jp LongBench ベンチマークは，日本語大型言語モデル（LLM）のロングコンテキスト処理能力を体系的に評価することを目的としており，複数の長文コンテキスト QA データセットを含んでいます。
本データセットはその一つです。
データセット概要
本データセットは、日本語の説明可能マルチホップ質問応答データセットJEMHopQA
（Ishii et al., 2024）を基に、Wikipedia記事を付与することで構築したロングコンテキストQA評価用データセットです。
最大65… See the full description on the dataset page: https://huggingface.co/datasets/llm-jp/llm-jp-longbench-JEMHop.llm-jp-longbench-NIILC
llm-jp-longbench-NIILC
llm-jp LongBench ベンチマークについて
このデータセットは，GitHub リポジトリhttps://github.com/llm-jp/llm-jp-longbenchで公開されているllm-jp LongBenchベンチマークの評価対象データセットの一部として構築されています。
llm-jp LongBench ベンチマークは，日本語大型言語モデル（LLM）のロングコンテキスト処理能力を体系的に評価することを目的としており，複数の長文コンテキスト QA データセットを含んでいます。
本データセットはその一つです。
データセット概要
本データセットは，日本語質問応答データセット NIILC
（Sekine, 2003）を基に，
回答が一意に定まり，かつ時間によって正解が変化しない質問のみを選別し，
それらに対応する Wikipedia 記事をコンテキストとして付与することで構築した，
ロングコンテキスト QA 評価用データセットです。… See the full description on the dataset page: https://huggingface.co/datasets/llm-jp/llm-jp-longbench-NIILC.Bitext-travel-llm-chatbot-training-dataset
Bitext - Travel Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [Travel] sector can be easily achieved using our two-step approach to LLM Fine-Tuning. An overview of… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-travel-llm-chatbot-training-dataset.jgpqa
JGPQA
This repository provides GPQA dataset translated from English into Japanese by LLM-jp, a collaborative project launched in Japan.
The dataset was translated from English to Japanese using machine translation, then checked and corrected by external experts.
The links of the original GPQA dataset are here(HuggingFace).
Send Questions to
llm-jp(at)nii.ac.jp
Model Card Authors
Yuji Tamakoshi, Kouta Nakayama, Yusuke Miyao.
JMedQA
JMedQA: Benchmarking Large Language Models and Vision-Language Models on the Japanese Medical Licensing Examination
JMedQA is a Japanese medical question-answering benchmark derived from Japan's National Medical Examination materials publicly released by the Ministry of Health, Labour and Welfare (MHLW).
The dataset supports both text-only large language model (LLM) evaluation and vision-language model (VLM) evaluation using associated examination images.
Its image-dependency… See the full description on the dataset page: https://huggingface.co/datasets/SIP-med-LLM/JMedQA.pubmedqa-recursive-llm-degradation-qwen2.5-0.5b
PubMedQA Recursive LLM Degradation — Qwen2.5-3B
This repository contains synthetic biomedical question-answering data
and model predictions generated as part of a study of recursive
fine-tuning and model degradation.
Base Model
Qwen/Qwen2.5-3B
Source Dataset
The experiments use the PubMedQA dataset:
qiaoxin/PubMedQA
This repository contains generated/derived research artifacts and does
not redistribute the original PubMedQA dataset in its entirety.… See the full description on the dataset page: https://huggingface.co/datasets/chrislimbe/pubmedqa-recursive-llm-degradation-qwen2.5-0.5b.pubmedqa-recursive-llm-degradation-qwen2.5-3b
PubMedQA Recursive LLM Degradation — Qwen2.5-3B
This repository contains synthetic biomedical question-answering data
and model predictions generated as part of a study of recursive
fine-tuning and model degradation.
Base Model
Qwen/Qwen2.5-3B
Source Dataset
The experiments use the PubMedQA dataset:
qiaoxin/PubMedQA
This repository contains generated/derived research artifacts and does
not redistribute the original PubMedQA dataset in its entirety.… See the full description on the dataset page: https://huggingface.co/datasets/chrislimbe/pubmedqa-recursive-llm-degradation-qwen2.5-3b.synthetic_multilingual_llm_prompts
Image generated by DALL-E. See prompt for more details
📝🌐 Synthetic Multilingual LLM Prompts
Welcome to the "Synthetic Multilingual LLM Prompts" dataset! This comprehensive collection features 1,250 synthetic LLM prompts generated using Gretel Navigator, available in seven different languages. To ensure accuracy and diversity in prompts, and translation quality and consistency across the different languages, we employed Gretel Navigator both as a generation tool and as an… See the full description on the dataset page: https://huggingface.co/datasets/gretelai/synthetic_multilingual_llm_prompts.llm-metric-mmluBitext-mortgage-loans-llm-chatbot-training-dataset
Bitext - Mortgage and Loans Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [Mortgage and Loans] sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-mortgage-loans-llm-chatbot-training-dataset.Bitext-wealth-management-llm-chatbot-training-dataset
Bitext - Wealth Management Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [Wealth Management] sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-wealth-management-llm-chatbot-training-dataset.TARA_Turkish_LLM_Benchmark
TARA: Turkish Advanced Reasoning Assessment Veri Seti
*Img Credit: Open AI ChatGPT
**English version is given below.**
Evaluation Notebook / Değerlendirme Not Defteri
Dataset Summary
TARA (Turkish Advanced Reasoning Assessment), Türkçe dilindeki Büyük Dil Modellerinin (LLM'ler) gelişmiş akıl yürütme yeteneklerini çoklu alanlarda ölçmek için tasarlanmış, zorluk derecesine göre sınıflandırılmış bir benchmark veri setidir. Bu veri seti, LLM'lerin sadece bilgi… See the full description on the dataset page: https://huggingface.co/datasets/emre/TARA_Turkish_LLM_Benchmark.Bitext-hospitality-llm-chatbot-training-dataset
Bitext - Hospitality Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [hospitality] sector can be easily achieved using our two-step approach to LLM Fine-Tuning. An… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-hospitality-llm-chatbot-training-dataset.Bitext-media-llm-chatbot-training-dataset
Bitext - Media Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [media] sector can be easily achieved using our two-step approach to LLM Fine-Tuning. An overview of… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-media-llm-chatbot-training-dataset.Bitext-restaurants-llm-chatbot-training-dataset
Bitext - Restaurants Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [restaurants] sector can be easily achieved using our two-step approach to LLM Fine-Tuning. An… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-restaurants-llm-chatbot-training-dataset.turkish_llm_finetune_dataset_4_topics
Turkish LLM Finetune Dataset - 4 Topics
This dataset is designed to fine-tune the T3 AI Turkish LLM. It was created by Barathan Aslan, Ömer Faruk Çelik, and Batuhan Kalem for the T3 AI Hackathon. The dataset focuses on four distinct topics: Agriculture, Sustainability, Turkish Education Sytem, and Turkish Law System.
Contributors
Barathan Aslan (https://huggingface.co/barathanasln)
Batuhan Kalem(https://huggingface.co/Pancarsuyu)
Ömer Faruk Çelik… See the full description on the dataset page: https://huggingface.co/datasets/barathanasln/turkish_llm_finetune_dataset_4_topics.llm-misinformation-resistance-index
LLM Misinformation Resistance Index (LMRI)
Formal name: LLM Misinformation Resistance Index (LMRI).
Public alias: the Gaslighting Index — the two headline scores keep their code
names GI-basic and GI-strict, where "GI" comes from the benchmark's public alias.
LMRI measures whether a language model will stand up to its own misinformation.
Each benchmark item is a fabricated conversation in which the assistant's own prior
turn contains a planted false claim (or, for controls, a… See the full description on the dataset page: https://huggingface.co/datasets/buildwithdmytro/llm-misinformation-resistance-index.Bitext-customer-support-llm-chatbot-training-dataset
Bitext - Customer Service Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the Customer Support sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/abhi23457/Bitext-customer-support-llm-chatbot-training-dataset.LLM_SQL_BaseDatosEspanol
Usos
Usos directos
El objetivo principal de este dataset es proporcionar ejemplos simples para el fine-tuning de modelos
de procesamiento de lenguaje natural (NLP) en el contexto de consultas SQL.
Usos fuera de mira
Podria usarse para el entrenamiento de una IA que sirva como creadora de base de datos artificiales
Estructura del conjunto de datos
Question: Es la pegunta que el usuario le dara al chatbot
Answer: La respuesta el que chatbot le… See the full description on the dataset page: https://huggingface.co/datasets/somosnlp/LLM_SQL_BaseDatosEspanol.H-LLMC2
Human Large Language Model Comparison Corpus (H-LLMC2)
H-LLMC2 presents a sample of HC3 [1], extended to seven open-weight medium-sized LLMs.
Description
H-LLMC2 is based on a sample of HC3, balanced over the 5 data sources (reddit_eli5, finance, medicine, open_qa, and wiki_csai).
842 questions are sampled from each source (i.e, the full set of wiki_csai, and random samples for the other source), yielding a parallel dataset of 4210 question-answers sets.
In addition to… See the full description on the dataset page: https://huggingface.co/datasets/noepsl/H-LLMC2.El-TARA_Spanish_LLM_Benchmark
El-Tara: Evaluación de Razonamiento Avanzado en Español
Dataset Summary
El-Tara (Evaluación de Razonamiento Avanzado en Español) is a benchmark dataset designed to assess the advanced reasoning capabilities of Large Language Models (LLMs) in Spanish. It is adapted from the original TARA (Turkish Advanced Reasoning Assessment) dataset.
Similar to TARA, El-Tara aims to test higher-order cognitive skills across multiple domains, using synthetically generated questions… See the full description on the dataset page: https://huggingface.co/datasets/emre/El-TARA_Spanish_LLM_Benchmark.