Transformers: Ship AI Models in Minutes, Not Months
Quickly define, train, and deploy state-of-the-art machine learning models across text, vision, and audio. Perfect for AI founders building intelligent applications.
161,190 stars33,383 forksPythonUpdated 6/2/2026100% free · open source
What it does
Transformers allows you to quickly define, train, and deploy state-of-the-art machine learning models for text, vision, and audio applications
When to use it
•Building a chatbot that requires natural language processing
•Creating a computer vision model for image classification
•Developing an audio classification model for music or voice recognition
Quick start
1Import the library with `from transformers import AutoModelForSequenceClassification, AutoTokenizer`
2Load a pre-trained model and tokenizer with `model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased')` and `tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased')`
3Prepare your dataset by tokenizing your text data with `inputs = tokenizer(text, return_tensors='pt')`
4Train your model with `outputs = model(**inputs)` and `loss = outputs.loss`
5Evaluate your model with `model.eval()` and `predictions = model(**inputs)`
Ready-to-paste prompt
python -c 'from transformers import pipeline; classifier = pipeline("sentiment-analysis"); print(classifier("I love this product!"))'
Topics
audio
deep-learning
deepseek
gemma
glm
hacktoberfest
llm
machine-learning
model-hub
natural-language-processing
nlp
pretrained-models
python
pytorch
pytorch-transformers
qwen
speech-recognition
transformer
vlm
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