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Attrprompt

AttrPrompt is an open-source Python framework and dataset repository for developing and evaluating large-language-model training-data generation pipelines, with code for generating data and training classifiers and published datasets for text-classification tasks.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Attrprompt — OpenViST project profile in AI Data & Analytics.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use AttrPrompt when you need a research-oriented framework, codebase or published dataset for LLM-generated text-classification training data.

Who it is for

  • Researchers studying synthetic or attributed training data for text classification.
  • Machine-learning practitioners exploring generated examples for classifier training.
  • Teams comparing training-data generation approaches across text domains.

Documented uses

  • Load original train, validation and test splits from published datasets.
  • Load named generated-data files for supported tasks.
  • Explore code for generating training data and training classifiers.
  • Compare data-generation approaches across news, reviews, social media, forums and research papers.
Before you choose

Requirements & limits

The public GitHub repository is licensed under Apache-2.0. The README links datasets hosted on Hugging Face and provides Python loading examples.

  • The repository covers both generated training data and classifier-training code.
  • Published tasks include multiclass classification and multilabel arXiv classification.
  • The README documents dataset-loading examples, but points to separate directories for generation and classifier-training implementation details.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Load an AttrPrompt dataset split

Load a published dataset split from Hugging Face for inspection or downstream work.

Before you start

  • Python
  • The Hugging Face datasets library

Steps

  1. Import load_dataset from datasets.

  2. Call load_dataset("yyu/nyt-attrprompt", split="train") to load the NYT training split.

  3. Use split="valid" or split="test" to load the corresponding splits.

Expected outcome

The selected NYT dataset split is available as a loaded dataset object.

Guide limitations

  • The README demonstrates this example for NYT; it does not document installation steps for the library.
  • This recipe loads dataset splits and does not run data generation or train a classifier.
Documentation 1 ↗
Source-based practical guide

Load a named generated-data file

Load the AttrPrompt-generated NYT training data file from the dataset repository.

Before you start

  • Python
  • The Hugging Face datasets library

Steps

  1. Import load_dataset from datasets.

  2. Call load_dataset("yyu/nyt-attrprompt", data_files="attrprompt-v1.jsonl", split="train").

Expected outcome

The named `attrprompt-v1.jsonl` file is loaded as a training split.

Guide limitations

  • The README’s command is specific to this named file and dataset repository.
  • The recipe loads existing data; it does not generate a new dataset.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
16 October 2026
Original website title
yueyu1030/AttrPrompt · Official homepage title · retained in its source language
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