Transformers is an open-source Python library for loading pretrained transformer models, tokenizers, and training utilities. It turns large-model capabilities into practical workflows for classification, sentiment analysis, moderation, semantic search, question answering, and text generation. section 9.3.3 section 10.2.1
A typical workflow starts with a pretrained model and its matching tokenizer. The tokenizer converts text into model-specific token IDs; the model returns encodings, logits, or generated token IDs that can be decoded back into text. section 10.2.1 section 9.3.3
Common library workflows include:
- Load a pretrained BERT model with BertModel.from_pretrained('bert-base-uncased') and inspect its architecture. section 9.3.3
- Fine-tune GPT-2 by wrapping tokenized lines in a PyTorch Dataset, splitting training and evaluation data, batching with a data collator, and training with Trainer. section 10.2.2
A minimal GPT-2 generation workflow
from transformers import GPT2Tokenizer, GPT2LMHeadModel tokenizer = GPT2Tokenizer.from_pretrained('gpt2') model = GPT2LMHeadModel.from_pretrained('gpt2') inputs = tokenizer.encode('NLP is', return_tensors='pt') outputs = model.generate(inputs, max_length=50) print(tokenizer.decode(outputs.squeeze(), skip_special_tokens=True)) The generated continuation may be fluent yet incorrect or repetitive, so outputs require evaluation rather than blind trust.
