Hugging Face
Hugging Face is the GitHub of AI hosting 500,000+ models, 100,000+ datasets, and the Transformers library. The go-to platform for open-source ML model discovery, fine-tuning, and deployment.
Contents 2 Sections
1Transformers Pipeline
from transformers import pipeline# Pipeline API highest level classifier = pipeline("sentiment-analysis") result = classifier(["I love this!", "The service was terrible."])
generator = pipeline("text-generation", model="gpt2", max_new_tokens=50) result = generator("The future of AI is")
qa = pipeline("question-answering", model="deepset/roberta-base-squad2") result = qa( question="What is Hugging Face?", context="Hugging Face hosts AI models and provides ML libraries." )
# NER ner = pipeline("ner", aggregation_strategy="simple") result = ner("Apple was founded by Steve Jobs in Cupertino.")
# Zero-shot zs = pipeline("zero-shot-classification") result = zs("This movie was amazing!", candidate_labels=["positive", "negative", "neutral"]) ```
2Fine-tuning
from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer
from datasets import load_datasetmodel_name = "distilbert-base-uncased" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)
dataset = load_dataset("imdb") def tokenize(examples): return tokenizer(examples["text"], truncation=True, padding=True, max_length=512) tokenized = dataset.map(tokenize, batched=True)
args = TrainingArguments( output_dir="./my-model", num_train_epochs=3, per_device_train_batch_size=16, evaluation_strategy="epoch", ) trainer = Trainer(model=model, args=args, train_dataset=tokenized["train"].select(range(1000)), eval_dataset=tokenized["test"].select(range(200))) trainer.train()
# Push to Hub model.push_to_hub("my-username/my-sentiment-model") tokenizer.push_to_hub("my-username/my-sentiment-model") ```
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