Welcome to the Hugging Face Transformers documentation! This guide will help you understand how to use the Transformers library, which provides state-of-the-art pre-trained models for natural language processing tasks.
Getting Started
Before diving into the details, make sure you have the Transformers library installed:
pip install transformers
Models
The Transformers library offers a wide range of pre-trained models for various NLP tasks. Here are some popular models:
- BERT: A general-purpose pre-trained language representation model.
- GPT-2: A transformer-based language model that generates text.
- RoBERTa: An optimized version of BERT for natural language understanding and generation.
For more information about the available models, visit the Transformers Models page.
Installation
To install the Transformers library, use the following command:
pip install transformers
Usage
Here's an example of how to use the BERT model for text classification:
from transformers import BertTokenizer, BertForSequenceClassification
# Load the tokenizer and model
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
# Tokenize the input text
inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
# Predict the class
outputs = model(**inputs)
# Get the predicted class
predicted_class = outputs.logits.argmax(-1).item()
print(f"Predicted class: {predicted_class}")
For more examples and tutorials, check out the Transformers Examples page.
Resources
If you have any questions or need further assistance, feel free to reach out to the Hugging Face community on Stack Overflow.
License
The Transformers library is released under the Apache 2.0 license.
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BERT Model Architecture