# ThilinaRajapakse/simpletransformers

Transformers for Information Retrieval, Text Classification, NER, QA, Language Modelling, Language Generation, T5, Multi-Modal, and Conversational AI

Repository: https://github.com/ThilinaRajapakse/simpletransformers
Canonical: https://ross.abutalabs.com/products/simpletransformers
Homepage: https://simpletransformers.ai/
Language: Python
License: Apache-2.0
License Family: permissive
Topics: transformers, text-classification, named-entity-recognition, question-answering, conversational-ai, information-retrival
Last push: 2026-05-31T18:18:50+00:00

## Health v2 (maintenance only)
Score: 61/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 85, release rhythm 8, longevity 100
- inputs: {"age_days": 2525, "days_push": 94, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4254, forks 712 (observed 2026-08-28T04:08:40.475371+00:00)

## What it is
Simple Transformers is a Python library built on Hugging Face Transformers that lets users train, fine-tune, and evaluate Transformer models with just a few lines of code. It supports tasks including text classification, NER, question answering, language generation, T5, dense retrieval, multi-modal classification, and conversational AI.

## Use cases
- fine-tune a transformer for text classification in a few lines of code
- train a named entity recognition model
- build a question answering system with BERT-style models
- fine-tune T5 for seq2seq tasks
- train dense retrieval models for information retrieval
- run language model generation and fine-tuning
- experiment with multi-modal classification
- prototype conversational AI models quickly

## When to choose
- you want a simple high-level API over Hugging Face Transformers for training and evaluation
- you need quick prototyping of standard NLP tasks like classification, NER, or QA
- you want built-in experiment tracking with Weights & Biases
- you prefer minimal boilerplate over writing custom PyTorch training loops

## When to avoid
- you need full control over model internals, custom architectures, or training loops
- you require the latest cutting-edge model support immediately after release
- you need production-grade inference serving rather than training workflows

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, nlp, llm-training, llm-inference, rag
- domain: machine-learning, deep-learning, large-language-models, artificial-intelligence
- platform: python, cross-platform
- tags: transformers, huggingface, text-classification, named-entity-recognition, question-answering, seq2seq, t5, conversational-ai, information-retrieval, fine-tuning, natural-language-processing, gpu

## Member repositories
- ThilinaRajapakse/simpletransformers (main) score 61

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:40.475371+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:22:09.414487+00:00, confidence not recorded.
  - readme: https://github.com/ThilinaRajapakse/simpletransformers (fetched 2026-08-28T04:08:40.475371+00:00, sha a060c40439ae)
  - homepage: https://simpletransformers.ai/ (fetched 2026-08-29T09:11:51.991465+00:00, sha b81a72623298)
- Data as of 2026-08-30T08:39:29.467469+00:00.