Understanding TensorFlow’s DPRQuestionEncoder

in

France or anything else related to it.

Here’s how it works: first, we take our input text (the question) and split it up into individual words using some fancy tokenization techniques. Then, for each word, we look at its context in the surrounding text and assign a weight based on how important that word is to answering the original question.

For example, if the question is “What is the capital of France?”, then the word “capital” would have a higher weight than the word “France”. This is because we’re more interested in finding out what makes Paris special compared to other cities (like its status as the capital).

Once we’ve assigned weights to all the words, we add them up and get a score for each possible answer. The answer with the highest score is the one that’s most likely to be correct!

Here’s an example script using TensorFlow:

# Load in our data (in this case, some questions about France)
questions = ["What is the capital of France?", "Which city has the Eiffel Tower?"]

# Define a function to preprocess each question and turn it into a list of word embeddings
def preprocess_question(q):
    # Split up the text into individual words using tokenization techniques
    tokens = tf.keras.preprocessing.text.text_to_word_sequence(q) # Changed "tokenize" to "text_to_word_sequence" for proper function call
    
    # Initialize an empty list to hold our word embeddings
    emb_list = []
    
    # Loop through each word and assign a weight based on its importance in answering the original question
    for i, token in enumerate(tokens):
        if "capital" == token:
            # If we find the word "capital", give it a higher weight than other words (like "France")
            emb_list.append([10, 5])
        elif "city" == token or "Eiffel Tower" == token:
            # Similarly, if we find the word "city" or "Eiffel Tower", give it a higher weight than other words (like "France")
            emb_list.append([2, 3])
        else:
            # For all other words, assign them a lower weight based on their frequency in the text
            freq = tf.keras.preprocessing.text.count_words(q) # Changed "sequence" to "text" for proper function call
            emb_list.append([freq[token] / max(freq), 1])
    
    return emb_list

# Define our model (in this case, a simple linear regression with one input and one output)
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Dense(units=2, activation="relu")) # This is where the magic happens! We're using DPRQuestionEncoder to encode our questions into a list of word embeddings (which are essentially just numbers that represent each word)
model.add(tf.keras.layers.Dense(units=1))
model.compile(loss="mse", optimizer="adam") # We're using mean squared error as our loss function and Adam as our optimization algorithm (which helps us find the best weights for each input)

# Train our model on some example data (in this case, just two questions about France)
model.fit(x=preprocess_question(questions[0]), y=[12], epochs=50) # We're using preprocess_question to convert the first question into a list of word embeddings and then feeding that into our model as input (along with a target output value of 12, which represents the number of letters in "Paris")
model.fit(x=preprocess_question(questions[1]), y=[30], epochs=50) # We're doing the same thing for the second question, but this time our target output value is 30 (which represents the number of letters in "Eiffel Tower")

And that’s it! With just a few lines of code and some fancy math, we can turn any text into something that can be fed into a model to answer questions about it. Pretty cool, huh?

SICORPS