datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Machine_Mindset_MBTI_datasetHere are the behavior datasets used for supervised fine-tuning (SFT). And they can also be used for direct preference optimization (DPO).
The exact copy can also be found in Github.
Prefix 'en' denotes the datasets of the English version.
Prefix 'zh' denotes the datasets of the Chinese version.
Dataset introduction
There are four dimension in MBTI. And there are two opposite attributes within each dimension.
To be specific:
Energe: Extraversion (E) - Introversion (I)… See the full description on the dataset page: https://huggingface.co/datasets/pandalla/Machine_Mindset_MBTI_dataset.lm-eval-results-ichigoberry-pandafish-dt-7b-private
Dataset Card for Evaluation run of ichigoberry/pandafish-dt-7b
Dataset automatically created during the evaluation run of model ichigoberry/pandafish-dt-7b
The dataset is composed of 62 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 2 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 is always pointing to the latest results.
An… See the full description on the dataset page: https://huggingface.co/datasets/nyu-dice-lab/lm-eval-results-ichigoberry-pandafish-dt-7b-private.chinese_law_examples
1000 examples of law items
law_item.jsonl contains 1000 samples of current and effective Chinese laws. e.g.
{"title": "《中华人民共和国劳动合同法(2012修正)》",
"classification": "类别 : 劳动合同营商环境优化 ",
"num": "第十九条",
"contents": "第十九条【试用期】劳动合同期限三个月以上不满一年的，试用期不得超过一个月；劳动合同期限一年以上不满三年的，试用期不得超过二个月；三年以上固定期限和无固定期限的劳动合同，试用期不得超过六个月。同一用人单位与同一劳动者只能约定一次试用期。以完成一定工作任务为期限的劳动合同或者劳动合同期限不满三个月的，不得约定试用期。试用期包含在劳动合同期限内。劳动合同仅约定试用期的，试用期不成立，该期限为劳动合同期限。"}
Using BGE Embedding to compute similarity between query and… See the full description on the dataset page: https://huggingface.co/datasets/pandalla/chinese_law_examples.panda
Dataset Card for PANDA
Dataset Summary
PANDA (Perturbation Augmentation NLP DAtaset) consists of approximately 100K pairs of crowdsourced human-perturbed text snippets (original, perturbed). Annotators were given selected terms and target demographic attributes, and instructed to rewrite text snippets along three demographic axes: gender, race and age, while preserving semantic meaning. Text snippets were sourced from a range of text corpora (BookCorpus, Wikipedia, ANLI… See the full description on the dataset page: https://huggingface.co/datasets/facebook/panda.megazeka-tr-spellfix-pairs
Megazeka · Turkish Spelling Correction Pairs
40,313 (misspelled → correct) Turkish sentence pairs with a full record of every corruption that
was applied, generated from the CC0 Common Voice Turkish Sentence Collector and from project-authored
everyday first/second-person sentences.
The distinguishing feature is the operations field: each pair carries the exact sequence of noise
transformations that produced it, with before/after text at each step. That makes it possible to… See the full description on the dataset page: https://huggingface.co/datasets/pandakingpunc/megazeka-tr-spellfix-pairs.pandas-create-context
Overview
This dataset is built from sql-create-context, which in itself builds from WikiSQL and Spider.
I have used GPT4 to translate the SQL schema into pandas DataFrame schem initialization statements and to translate the SQL queries into pandas queries.
There are 862 examples of natural language queries, pandas DataFrame creation statements, and pandas query answering the question using the DataFrame creation statement as context. This dataset was built with text-to-pandas… See the full description on the dataset page: https://huggingface.co/datasets/hiltch/pandas-create-context.chinese_verdict_examples
verdicts examples
verdicts_200.jsonl contains 200 examples of verdicts from Chinese Judgements Online, we process the datasets for semantic retrieval
using BGE to compute similarity between query and verdict
from FlagEmbedding import FlagModel
from datasets import load_dataset
dataset = load_dataset("FarReelAILab/verdicts")
model = FlagModel('BAAI/bge-large-zh-v1.5',
query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：",
use_fp16=True)… See the full description on the dataset page: https://huggingface.co/datasets/pandalla/chinese_verdict_examples.pandas-tasks-v1
pandas-tasks-v1
Task dataset for a pandas RL / eval environment, in the shape used by the
Prime Intellect Environments Hub.
43 pandas DataFrame manipulation tasks across 6 categories. Each task gives the model one or
more input frames and an instruction; the answer is the frame left in df, graded with
pandas.testing.assert_frame_equal against a reference result. Grading is fully
deterministic — no LLM judge, no external API.
Category
Tasks
Covers
transformation
10… See the full description on the dataset page: https://huggingface.co/datasets/eltociear/pandas-tasks-v1.datatager_standard_med_question
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Standard Medical Question
Prompt for Training
When training your model with this dataset, prepend the following prompt to each input instance:
你需要将医疗领域中的冗长或复杂的患者咨询文本转换为简洁、结构化的问题表达。请确保输出文本保留所有关键的医疗信息，去除重复或不必要的细节，并使用专业的医疗术语准确描述患者的情况和需求。
Description
AnyTaskTune is a publication by the DataTager team. We advocate for rapid training of large models suitable for specific business… See the full description on the dataset page: https://huggingface.co/datasets/pandalla/datatager_standard_med_question.datatager_symptom_recognition_and_advice
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Extract Medical Information Dataset
Prompt for Training
When training your model with this dataset, prepend the following prompt to each input instance:
你需要去做的是理解患者的咨询文本，并基于这些症状提供一个可能的医学解释以及相应的建议措施。请始终确保你的输出中包括以下元素：1. 对输入中提到的症状的识别和确认。2. 基于症状的可能医学解释。3. 针对进一步诊断或治疗的建议措施。
Description
AnyTaskTune is a publication by the DataTager team. We advocate for rapid training of large… See the full description on the dataset page: https://huggingface.co/datasets/pandalla/datatager_symptom_recognition_and_advice.datatager_extract_med_information
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Extract Medical Information Dataset
Prompt for Training
When training your model with this dataset, prepend the following prompt to each input instance:
你需要从用户描述中提取三到六个关键的医疗信息，并以结构化的方式输出，以便快速理解用户的健康状况和相关的疑问。
Description
AnyTaskTune is a publication by the DataTager team. We advocate for rapid training of large models suitable for specific business scenarios through… See the full description on the dataset page: https://huggingface.co/datasets/pandalla/datatager_extract_med_information.datatager_clinical_question_enhancement
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Clinical Question Enhancement Dataset
Prompt for Training
When training your model with this dataset, prepend the following prompt to each input instance:
请对患者如下的咨询做详细分析，反问四到六个细节问题，要求患者补充详细信息，
Description
AnyTaskTune is a publication by the DataTager team. We advocate for rapid training of large models suitable for specific business scenarios through task-specific… See the full description on the dataset page: https://huggingface.co/datasets/pandalla/datatager_clinical_question_enhancement.datatager_llm_resume_scoring
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Large Language Model Resume Scoring (LLM-RS) Task Dataset
Prompt for Training
When training your model with this dataset, prepend the following prompt to each input instance:
给定一个候选人的工作经历信息，你需要针对每个职位进行综合评分。每个工作经历包括职位名称、工作内容、技能需求等详细描述。根据职位的特性和需求，你应该为每个工作经历设计不同的评分标准。
针对每个工作经历，基于上述评分方面，给出一个具体的分数（1-10分）。每个评分方面的最高分为10分，确保评分具有差异性，反映出候选人在每个岗位上的表现强度和改进空间。
Description… See the full description on the dataset page: https://huggingface.co/datasets/pandalla/datatager_llm_resume_scoring.whatsmachinelearningSimple-Datasets-for-Pandas
Simple Datasets for Pandas 🐼
A collection of simple and beginner-friendly datasets designed for learning and practicing Pandas and basic data analysis with Python.
📦 Dataset Contents
This dataset contains data available in three common file formats:
students.csv - CSV format
students.json - JSON format
students.xlsx - Microsoft Excel format
These files can be used to practice loading, exploring, cleaning, manipulating, and analyzing tabular data using Pandas.… See the full description on the dataset page: https://huggingface.co/datasets/Qamro/Simple-Datasets-for-Pandas.datatager_legal_split_cases
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Legal Split Cases Dataset
Description
AnyTaskTune is a publication by the DataTager team. We advocate for rapid training of large models suitable for specific business scenarios through task-specific fine-tuning. We have open-sourced several datasets across various domains such as legal, medical, education, and HR, and this dataset is one of them.
The Legal Split dataset is a collection… See the full description on the dataset page: https://huggingface.co/datasets/pandalla/datatager_legal_split_cases.datatager_legal_question_enhancement
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Legal Question Enhancement Dataset
Description
AnyTaskTune is a publication by the DataTager team. We advocate for rapid training of large models suitable for specific business scenarios through task-specific fine-tuning. We have open-sourced several datasets across various domains such as legal, medical, education, and HR, and this dataset is one of them.
The Legal Question Enhancement… See the full description on the dataset page: https://huggingface.co/datasets/pandalla/datatager_legal_question_enhancement.sejarah-qa-16042025datatager_hospital_service_feedback_analysis
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Hospital Service Feedback Analysis
Prompt for Training
When training your model with this dataset, prepend the following prompt to each input instance:
根据提供的酒店评论文本，自动识别出评论中的关键特征，并对每一个关键特征进行100分制的评分。同时，需要为每个关键特征提供一段解释，说明为何给出该评分。
Description
AnyTaskTune is a publication by the DataTager team. We advocate for rapid training of large models suitable for specific business… See the full description on the dataset page: https://huggingface.co/datasets/pandalla/datatager_hospital_service_feedback_analysis.datatager_social_and_emotional_risk_assessment
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Social and Emotional Risk Assessment
Prompt for Training
When training your model with this dataset, prepend the following prompt to each input instance:
根据提供的心理咨询问题，自动生成包含风险评估和建议的回答。识别咨询中的关键信息，评估咨询者可能的心理状态，然后提供针对性的支持和建议。
Description
AnyTaskTune is a publication by the DataTager team. We specialize in rapidly training large models suitable for specific business scenarios… See the full description on the dataset page: https://huggingface.co/datasets/pandalla/datatager_social_and_emotional_risk_assessment.datatager_standard_finance_question
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Standard Finance Question
Prompt for Training
When training your model with this dataset, prepend the following prompt to each input instance:
将非标准或口语化的金融咨询转换为标准的、正式的语句。这一转换旨在清晰表达用户的咨询意图，同时提高语句的专业度和易理解性。
Description
AnyTaskTune is a publication by the DataTager team. We advocate for rapid training of large models suitable for specific business scenarios through task-specific… See the full description on the dataset page: https://huggingface.co/datasets/pandalla/datatager_standard_finance_question.sejarah-qa-15042025qwen3b_panda_numstest-small-datasetdatatager_legal_extract_information
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Legal Extract Information Dataset
Description
AnyTaskTune is a publication by the DataTager team. We advocate for rapid training of large models suitable for specific business scenarios through task-specific fine-tuning. We have open-sourced several datasets across various domains such as legal, medical, education, and HR, and this dataset is one of them.
The Legal Extract Information… See the full description on the dataset page: https://huggingface.co/datasets/pandalla/datatager_legal_extract_information.gemma4b_panda_numstool-calling-conversations-mrigh6o0
Tool Calling Conversations
An Arena-style dataset of anonymized, multi-turn conversations focused on real-world
tool use. It is intended for research, evaluation, and training of models that decide
when and how to call tools.
The conversations include:
Tool selection and no-tool decisions
Structured tool arguments
Sequential and parallel tool calls
Tool results and error recovery
Multi-step agent workflows
Final responses after tool execution
Data is organized into… See the full description on the dataset page: https://huggingface.co/datasets/dakr-pandas/tool-calling-conversations-mrigh6o0.pandas-plotly-code-datasetgemma4b_panda_cotllama8b_panda_nums