TensorFlow NLP (Natural Language Processing) is a suite of tools and resources provided by TensorFlow, which is an open-source machine learning framework developed by Google Brain. This documentation page aims to provide an overview of TensorFlow NLP resources available on this site.
Quick Links
Installation
To get started with TensorFlow NLP, you need to install TensorFlow. You can download and install the latest version from the TensorFlow official website.
Key Components
TensorFlow NLP includes several key components that enable developers and researchers to build NLP applications:
- Pre-trained Models: TensorFlow NLP provides pre-trained models that can be used directly in your applications. These models include BERT, GPT-2, and more.
- Text Preprocessing: Tools for tokenization, cleaning, and formatting text data.
- Transformers: A library for working with neural networks, particularly those that are based on transformer models.
- Keras Layers: Additional layers for NLP tasks, such as embedding layers, recurrent layers, and convolutional layers.
Getting Started
If you are new to TensorFlow NLP, we recommend starting with the TensorFlow NLP tutorial. This tutorial will guide you through the basics of using TensorFlow NLP to build an NLP application.
Resources
Example
Here's an example of how to use a pre-trained BERT model for sentiment analysis:
import tensorflow as tf
from transformers import BertTokenizer, TFBertForSequenceClassification
# Load pre-trained model and tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = TFBertForSequenceClassification.from_pretrained('bert-base-uncased')
# Tokenize and encode text
text = "I love TensorFlow NLP!"
encoded_input = tokenizer(text, return_tensors='tf')
# Predict sentiment
predictions = model(encoded_input)