Learn HUGGINGFACE-TRANSFORMERS with Real Code Examples

from transformers import pipeline

# Load sentiment-analysis pipeline
classifier = pipeline('sentiment-analysis')

# Analyze text
result = classifier('I love using Hugging Face Transformers!')
print(result)

A minimal example using Hugging Face Transformers to perform sentiment analysis on text.

from transformers import pipeline

# Load NER pipeline
ner_pipeline = pipeline('ner', grouped_entities=True)

# Analyze text
text = 'Hugging Face is based in New York.'
result = ner_pipeline(text)
print(result)

Uses a pretrained pipeline to identify named entities in text.

from transformers import pipeline

# Load question-answering pipeline
qa_pipeline = pipeline('question-answering')

context = 'Hugging Face develops state-of-the-art NLP models.'
question = 'What does Hugging Face develop?'

result = qa_pipeline(question=question, context=context)
print(result)

Answers questions based on a given context using a pretrained model.

from transformers import pipeline

# Load text-generation pipeline
generator = pipeline('text-generation', model='gpt2')

prompt = 'Once upon a time'
result = generator(prompt, max_length=50)
print(result)

Generates text continuations using a language model.

from transformers import pipeline

# Load translation pipeline
translator = pipeline('translation_en_to_fr')

text = 'I love machine learning.'
result = translator(text)
print(result)

Translates text from English to French using a pretrained model.

from transformers import pipeline

# Load summarization pipeline
summarizer = pipeline('summarization')

text = 'Hugging Face provides an open-source library that makes it easy to use state-of-the-art NLP models in Python applications.'
result = summarizer(text, max_length=50, min_length=25, do_sample=False)
print(result)

Summarizes long text using a pretrained summarization model.

from transformers import pipeline

# Load zero-shot classification pipeline
classifier = pipeline('zero-shot-classification')

text = 'I love programming in Python.'
candidate_labels = ['programming', 'sports', 'politics']

result = classifier(text, candidate_labels)
print(result)

Classifies text into user-defined labels without model retraining.

from transformers import AutoTokenizer, AutoModel
import torch

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased')
model = AutoModel.from_pretrained('distilbert-base-uncased')

text = 'Transformers are amazing!'
inputs = tokenizer(text, return_tensors='pt')
outputs = model(**inputs)

# Get sentence embedding
embedding = outputs.last_hidden_state.mean(dim=1)
print(embedding)

Extracts embeddings from text using a pretrained model.

from transformers import pipeline

# Load fill-mask pipeline
unmasker = pipeline('fill-mask')

text = 'Hugging Face is creating a [MASK] library.'
result = unmasker(text)
print(result)

Predicts masked words in a sentence using a pretrained model.

from transformers import pipeline, Conversation

# Load conversational pipeline
conversational_pipeline = pipeline('conversational')

conversation = Conversation('Hello! How are you?')
result = conversational_pipeline(conversation)
print(result)

Handles conversational input using a pretrained dialogue model.