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Simple Transformers

Simple Transformers is an open-source Python library based on Hugging Face Transformers that provides task-specific interfaces for training, evaluating and making predictions with transformer models.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Simple Transformers — OpenViST project profile in AI Developer Tools & SDKs.
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Understand the project

Practical overview

When it fits

Use it when you want a Python interface for a documented transformer workflow and can prepare data and model configuration for the selected task.

Who it is for

  • Python developers building transformer-model workflows for NLP tasks.
  • Researchers and practitioners prototyping classification, retrieval, NER, question-answering or language-generation models.

Documented uses

  • Train or fine-tune a task-specific transformer model.
  • Evaluate a model using task-appropriate evaluation data.
  • Generate predictions from unlabeled inputs.
  • Experiment with dense retrieval, conversational AI and multimodal classification.
Before you choose

Requirements & limits

The library is available as a public GitHub repository under the Apache-2.0 license and is installed with pip.

  • The library is built on Hugging Face Transformers and uses pretrained model types supported for the corresponding task.
  • The README documents CPU-only PyTorch and CUDA installation paths.
  • Each task has its own data formats and configuration options; consult the task-specific documentation before preparing inputs.
  • Weights & Biases is optional and provides experiment tracking and browser-based visualization.

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

Set up Simple Transformers with Conda

Prepare a Conda environment and install the Simple Transformers Python library.

Before you start

  • Anaconda or Miniconda
  • Python, pandas and tqdm in a Conda environment
  • PyTorch installed for either CPU-only or CUDA use

Steps

  1. Create and activate a Conda environment with Python, pandas and tqdm.

  2. Install PyTorch using the documented CPU-only option or the CUDA option.

  3. Install Simple Transformers with pip.

Expected outcome

A Conda environment with the library and its documented core dependencies installed.

Guide limitations

  • This covers installation only; model-specific data formats, configuration and use are covered in the task documentation.
  • Weights & Biases is a separate optional installation for experiment tracking.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
14 October 2026
Original website title
ThilinaRajapakse/simpletransformers · Official homepage title · retained in its source language
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