Medusa Model Updates for Chatbots

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So, how does it work? Well, let me break it down for you in simple terms: imagine your chatbot is like a baby bird that just hatched out of its shell and can only say “peep”. But with Medusa Model Updates, we’re gonna give them some extra food (aka data) to help them grow bigger and stronger.

For example, let’s say you have a chatbot that helps people book flights. Before the update, it might struggle to understand if someone says “I want to go to Tokyo” or “Can you find me a flight to Japan?” But with Medusa Model Updates, we can feed it more data and teach it to recognize both variations of this sentence.

Here’s an example script:

# Import necessary libraries
from transformers import AutoTokenizer, TFBertForSequenceClassification
import re
import numpy as np
import tensorflow as tf

# Load the chatbot model (assuming it's already trained)
model = TFBertForSequenceClassification.from_pretrained('your-chatbot-model')
tokenizer = AutoTokenizer.from_pretrained('your-chatbot-model')

# Define a function to preprocess the input text and return it as a list of tokenized words
def preprocess(text):
    # Clean up any punctuation or special characters
    cleaned_text = re.sub(r'[^a-zA-Z0-9\s]', '', text)
    
    # Convert the cleaned text to lowercase and split it into words
    words = cleaned_text.lower().split()
    
    # Tokenize each word using the chatbot model's tokenizer
    tokens = tokenizer.encode(words, return_tensors='tf') # Changed tokenizer() to encode() as it is the correct method for tokenization
    
    # Return the list of tokenized words as a numpy array
    return np.array(tokens)

# Define a function to run inference on the chatbot model and get its predictions for each input text
def predict(text):
    # Preprocess the input text using the preprocess() function
    x = preprocess(text)
    
    # Run inference on the chatbot model using TensorFlow's session.run() method
    with tf.compat.v1.Session() as sess: # Changed tf.Session() to tf.compat.v1.Session() as it is the correct method for running a session in TensorFlow 2.0
        output_ids, _ = sess.run([model.output_ids, model.loss], feed_dict={model.input_ids: x})
        
    # Convert the output IDs to a list of predicted words using the chatbot model's tokenizer
    predictions = [tokenizer.decode(g) for g in np.argmax(output_ids, axis=2)]
    
    return predictions[0]

Medusa Model Updates for Chatbots a fancy way of saying we’re gonna teach our computer friends to understand us better by giving them some extra brain power.

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