OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
AI Developer Tools & SDKs

Textbox

Textbox is an open-source Python and PyTorch library that provides a unified pipeline for applying pretrained language models to text-generation tasks, training models and evaluating generated text.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Textbox — OpenViST project profile in AI Developer Tools & SDKs.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Textbox when a Python/PyTorch project needs a shared workflow for pretrained-model generation, experimentation or evaluation across supported text-generation tasks.

Who it is for

  • Researchers comparing language models or datasets across text-generation tasks.
  • Developers building translation, story-generation or style-transfer prototypes.
  • Practitioners who need documented training and evaluation workflows alongside generation.

Documented uses

  • Run a pretrained language model on a supported text-generation dataset.
  • Train or pre-train models using documented objectives and training strategies.
  • Evaluate and inspect generated text with automatic metrics and visualization tools.
  • Structure experiments across multiple generation tasks, datasets and model families.
Before you choose

Requirements & limits

The public GitHub repository specifies the MIT License.

  • Textbox is a Python and PyTorch library, and its installation guidance recommends a Python 3.8 Conda environment.
  • The documented quick start requires selecting a model, model path and dataset; supported datasets are downloaded and stored in the repository's dataset folder.
  • The README describes 47 model or module options, 13 tasks and 83 datasets, plus 17 automatic evaluation metrics across four categories.
  • Training documentation includes four pre-training objectives and strategies such as distributed data parallel, efficient decoding, hyperparameter optimization and repeated experiments.
  • The installation instructions note that a modified version of Transformers is installed and identify a possible files2rouge XML::Parser dependency issue.

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

Run a pretrained model on a supported dataset

Use the documented quick-start workflow to run BART-base on the samsum dataset.

Before you start

  • A Python 3.8 Conda environment is recommended because installation includes a modified version of Transformers.
  • The Textbox repository cloned and installed with its install.sh script.
  • The samsum dataset downloaded from RUCAIBox on Hugging Face and placed under the repository's dataset folder.

Steps

  1. Create a Conda environment with Python 3.8 using the documented command: conda create -n TextBox python=3.8.

  2. Clone the repository, enter its directory and install it with bash install.sh.

  3. Download samsum and place it under the dataset folder, following the repository's dataset layout.

  4. Run the documented example: python run_textbox.py --model=BART --dataset=samsum --model_path=facebook/bart-base.

Expected outcome

The documented example runs Facebook BART-base with the samsum dataset through Textbox's text-generation pipeline.

Guide limitations

  • The repository notes a possible XML::Parser dependency error when installing files2rouge and links to an issue for troubleshooting.
  • The guide reflects the repository's documented commands; it does not verify a successful installation or run.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
16 October 2026
Original website title
RUCAIBox/TextBox · Official homepage title · retained in its source language
How we review sourcesSuggest a correction