knowledge_base/nlp/semantic_similarity_with_keras_hub.py

"""
Title: Semantic Similarity with KerasHub
Author: [Anshuman Mishra](https://github.com/shivance/)
Date created: 2023/02/25
Last modified: 2023/02/25
Description: Use pretrained models from KerasHub for the Semantic Similarity Task.
Accelerator: GPU
"""

"""
## Introduction

Semantic similarity refers to the task of determining the degree of similarity between two
sentences in terms of their meaning. We already saw in [this](https://keras.io/examples/nlp/semantic_similarity_with_bert/)
example how to use SNLI (Stanford Natural Language Inference) corpus to predict sentence
semantic similarity with the HuggingFace Transformers library. In this tutorial we will
learn how to use [KerasHub](https://keras.io/keras_hub/), an extension of the core Keras API,
for the same task. Furthermore, we will discover how KerasHub effectively reduces boilerplate
code and simplifies the process of building and utilizing models. For more information on KerasHub,
please refer to [KerasHub's official documentation](https://keras.io/keras_hub/).

This guide is broken down into the following parts:

1. *Setup*, task definition, and establishing a baseline.
2. *Establishing baseline* with BERT.
3. *Saving and Reloading* the model.
4. *Performing inference* with the model.
5  *Improving accuracy* with RoBERTa

## Setup

The following guide uses [Keras Core](https://keras.io/keras_core/) to work in
any of `tensorflow`, `jax` or `torch`. Support for Keras Core is baked into
KerasHub, simply change the `KERAS_BACKEND` environment variable below to change
the backend you would like to use. We select the `jax` backend below, which will
give us a particularly fast train step below.
"""

"""shell
pip install -q --upgrade keras-hub
pip install -q --upgrade keras  # Upgrade to Keras 3.
"""

import numpy as np
import tensorflow as tf
import keras
import keras_hub
import tensorflow_datasets as tfds

"""
To load the SNLI dataset, we use the tensorflow-datasets library, which
contains over 550,000 samples in total. However, to ensure that this example runs
quickly, we use only 20% of the training samples.

## Overview of SNLI Dataset

Every sample in the dataset contains three components: `hypothesis`, `premise`,
and `label`. epresents the original caption provided to the author of the pair,
while the hypothesis refers to the hypothesis caption created by the author of
the pair. The label is assigned by annotators to indicate the similarity between
the two sentences.

The dataset contains three possible similarity label values: Contradiction, Entailment,
and Neutral. Contradiction represents completely dissimilar sentences, while Entailment
denotes similar meaning sentences. Lastly, Neutral refers to sentences where no clear
similarity or dissimilarity can be established between them.
"""

snli_train = tfds.load("snli", split="train[:20%]")
snli_val = tfds.load("snli", split="validation")
snli_test = tfds.load("snli", split="test")

# Here's an example of how our training samples look like, where we randomly select
# four samples:
sample = snli_test.batch(4).take(1).get_single_element()
sample

"""
### Preprocessing

In our dataset, we have identified that some samples have missing or incorrectly labeled
data, which is denoted by a value of -1. To ensure the accuracy and reliability of our model,
we simply filter out these samples from our dataset.
"""


def filter_labels(sample):
    return sample["label"] >= 0


"""
Here's a utility function that splits the example into an `(x, y)` tuple that is suitable
for `model.fit()`. By default, `keras_hub.models.BertClassifier` will tokenize and pack
together raw strings using a `"[SEP]"` token during training. Therefore, this label
splitting is all the data preparation that we need to perform.
"""


def split_labels(sample):
    x = (sample["hypothesis"], sample["premise"])
    y = sample["label"]
    return x, y


train_ds = (
    snli_train.filter(filter_labels)
    .map(split_labels, num_parallel_calls=tf.data.AUTOTUNE)
    .batch(16)
)
val_ds = (
    snli_val.filter(filter_labels)
    .map(split_labels, num_parallel_calls=tf.data.AUTOTUNE)
    .batch(16)
)
test_ds = (
    snli_test.filter(filter_labels)
    .map(split_labels, num_parallel_calls=tf.data.AUTOTUNE)
    .batch(16)
)


"""
## Establishing baseline with BERT.

We use the BERT model from KerasHub to establish a baseline for our semantic similarity
task. The `keras_hub.models.BertClassifier` class attaches a classification head to the BERT
Backbone, mapping the backbone outputs to a logit output suitable for a classification task.
This significantly reduces the need for custom code.

KerasHub models have built-in tokenization capabilities that handle tokenization by default
based on the selected model. However, users can also use custom preprocessing techniques
as per their specific needs. If we pass a tuple as input, the model will tokenize all the
strings and concatenate them with a `"[SEP]"` separator.

We use this model with pretrained weights, and we can use the `from_preset()` method
to use our own preprocessor. For the SNLI dataset, we set `num_classes` to 3.
"""

bert_classifier = keras_hub.models.BertClassifier.from_preset(
    "bert_tiny_en_uncased", num_classes=3
)

"""
Please note that the BERT Tiny model has only 4,386,307 trainable parameters.

KerasHub task models come with compilation defaults. We can now train the model we just
instantiated by calling the `fit()` method.
"""

bert_classifier.fit(train_ds, validation_data=val_ds, epochs=1)

"""
Our BERT classifier achieved an accuracy of around 76% on the validation split. Now,
let's evaluate its performance on the test split.

### Evaluate the performance of the trained model on test data.
"""

bert_classifier.evaluate(test_ds)

"""
Our baseline BERT model achieved a similar accuracy of around 76% on the test split.
Now, let's try to improve its performance by recompiling the model with a slightly
higher learning rate.
"""

bert_classifier = keras_hub.models.BertClassifier.from_preset(
    "bert_tiny_en_uncased", num_classes=3
)
bert_classifier.compile(
    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    optimizer=keras.optimizers.Adam(5e-5),
    metrics=["accuracy"],
)

bert_classifier.fit(train_ds, validation_data=val_ds, epochs=1)
bert_classifier.evaluate(test_ds)

"""
Just tweaking the learning rate alone was not enough to boost performance, which
stayed right around 76%. Let's try again, but this time with
`keras.optimizers.AdamW`, and a learning rate schedule.
"""


class TriangularSchedule(keras.optimizers.schedules.LearningRateSchedule):
    """Linear ramp up for `warmup` steps, then linear decay to zero at `total` steps."""

    def __init__(self, rate, warmup, total):
        self.rate = rate
        self.warmup = warmup
        self.total = total

    def get_config(self):
        config = {"rate": self.rate, "warmup": self.warmup, "total": self.total}
        return config

    def __call__(self, step):
        step = keras.ops.cast(step, dtype="float32")
        rate = keras.ops.cast(self.rate, dtype="float32")
        warmup = keras.ops.cast(self.warmup, dtype="float32")
        total = keras.ops.cast(self.total, dtype="float32")

        warmup_rate = rate * step / self.warmup
        cooldown_rate = rate * (total - step) / (total - warmup)
        triangular_rate = keras.ops.minimum(warmup_rate, cooldown_rate)
        return keras.ops.maximum(triangular_rate, 0.0)


bert_classifier = keras_hub.models.BertClassifier.from_preset(
    "bert_tiny_en_uncased", num_classes=3
)

# Get the total count of training batches.
# This requires walking the dataset to filter all -1 labels.
epochs = 3
total_steps = sum(1 for _ in train_ds.as_numpy_iterator()) * epochs
warmup_steps = int(total_steps * 0.2)

bert_classifier.compile(
    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    optimizer=keras.optimizers.AdamW(
        TriangularSchedule(1e-4, warmup_steps, total_steps)
    ),
    metrics=["accuracy"],
)

bert_classifier.fit(train_ds, validation_data=val_ds, epochs=epochs)

"""
Success! With the learning rate scheduler and the `AdamW` optimizer, our validation
accuracy improved to around 79%.

Now, let's evaluate our final model on the test set and see how it performs.
"""

bert_classifier.evaluate(test_ds)

"""
Our Tiny BERT model achieved an accuracy of approximately 79% on the test set
with the use of a learning rate scheduler. This is a significant improvement over
our previous results. Fine-tuning a pretrained BERT
model can be a powerful tool in natural language processing tasks, and even a
small model like Tiny BERT can achieve impressive results.

Let's save our model for now
and move on to learning how to perform inference with it.

## Save and Reload the model
"""
bert_classifier.save("bert_classifier.keras")
restored_model = keras.models.load_model("bert_classifier.keras")
restored_model.evaluate(test_ds)

"""
## Performing inference with the model.

Let's see how to perform inference with KerasHub models
"""

# Convert to Hypothesis-Premise pair, for forward pass through model
sample = (sample["hypothesis"], sample["premise"])
sample

"""
The default preprocessor in KerasHub models handles input tokenization automatically,
so we don't need to perform tokenization explicitly.
"""
predictions = bert_classifier.predict(sample)


def softmax(x):
    return np.exp(x) / np.exp(x).sum(axis=0)


# Get the class predictions with maximum probabilities
predictions = softmax(predictions)

"""
## Improving accuracy with RoBERTa

Now that we have established a baseline, we can attempt to improve our results
by experimenting with different models. Thanks to KerasHub, fine-tuning a RoBERTa
checkpoint on the same dataset is easy with just a few lines of code.
"""

# Inittializing a RoBERTa from preset
roberta_classifier = keras_hub.models.RobertaClassifier.from_preset(
    "roberta_base_en", num_classes=3
)

roberta_classifier.fit(train_ds, validation_data=val_ds, epochs=1)

roberta_classifier.evaluate(test_ds)

"""
The RoBERTa base model has significantly more trainable parameters than the BERT
Tiny model, with almost 30 times as many at 124,645,635 parameters. As a result, it took
approximately 1.5 hours to train on a P100 GPU. However, the performance
improvement was substantial, with accuracy increasing to 88% on both the validation
and test splits. With RoBERTa, we were able to fit a maximum batch size of 16 on
our P100 GPU.

Despite using a different model, the steps to perform inference with RoBERTa are
the same as with BERT!
"""

predictions = roberta_classifier.predict(sample)
print(tf.math.argmax(predictions, axis=1).numpy())

"""
We hope this tutorial has been helpful in demonstrating the ease and effectiveness
of using KerasHub and BERT for semantic similarity tasks.

Throughout this tutorial, we demonstrated how to use a pretrained BERT model to
establish a baseline and improve performance by training a larger RoBERTa model
using just a few lines of code.

The KerasHub toolbox provides a range of modular building blocks for preprocessing
text, including pretrained state-of-the-art models and low-level Transformer Encoder
layers. We believe that this makes experimenting with natural language solutions
more accessible and efficient.
"""