Working with Hugging Face Models on Amazon SageMaker

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So, what exactly is this magical technology? Well, it’s basically a way to train and use machine learning models without having to worry about all the ***** details like setting up servers or managing infrastructure. And let me tell you, it’s pretty ***** awesome!

First how do we get started with Hugging Face on SageMaker? Well, there are a few different ways to go about this, but one of the easiest is by using their pre-trained models that are already available in their model hub. These models have been trained on massive datasets and can handle all sorts of tasks like text classification, sentiment analysis, and language translation.

To use these models, we simply need to create a new SageMaker notebook instance and then install the Hugging Face transformers library using pip (which is pretty much standard practice for Python). Once that’s done, we can load in our pre-trained model from the Hugging Face hub and start making predictions!

Here’s an example of what this might look like:

# Import necessary libraries
import sagemaker
from transformers import AutoTokenizer, TFBertForSequenceClassification

# Load the pre-trained BERT model for text classification from the Hugging Face hub
model_name = "bert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(model_name) # Load the tokenizer for the BERT model
model = TFBertForSequenceClassification.from_pretrained(model_name, num_labels=2) # Load the pre-trained BERT model with 2 labels (positive and negative)

# Load the input data (in this case, a list of sentences and their corresponding labels)
input_data = [("This is a great book!", 1), ("I hated that movie.", -1)]

# Preprocess the input data by tokenizing each sentence using the Hugging Face tokenizer
for text, label in input_data:
    encoded_text = tokenizer(text, return_tensors="tf") # Tokenize the input text using the tokenizer and convert it to a TensorFlow tensor
    
# Make predictions on the preprocessed input data using the loaded BERT model
predictions = model.predict(encoded_text) # Use the BERT model to make predictions on the preprocessed input data

And that’s it! With just a few lines of code, we can load in our pre-trained Hugging Face model and start making predictions on new text data. Pretty cool, right?

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