Building a Sentence Similarity Finder in Keras

What are We Building?

The task of determining the similarity in meaning between two sentences is known as Semantic Similarity. This guide will use the SNLI (Stanford Natural Language Inference) Corpus to predict the semantic similarity between sentences by fine-tuning a BERT model. The BERT model is a transformer-based pre-trained model that has achieved state-of-the-art results on many natural language processing tasks. By fine-tuning the BERT model on the SNLI corpus, we can train it to accept two sentences as inputs and output a similarity score, indicating how similar the two sentences are in semantics.

The SNLI corpus is a large dataset of sentence pairs, labeled with one of three labels: "entailment", "contradiction", or "neutral":, which can be used to train the BERT model to predict semantic similarity.

Pre-requisites

For building a simple Sentence Similarity in Keras, there are a few prerequisites which are listed below:

• Familiarity with Python and Keras:
  You should have a basic understanding of Python programming and be familiar with the Keras library, a high-level neural networks API that runs on top of TensorFlow or Theano.

• Dataset:
  You will need a dataset of pairs of sentences that you want to compare for similarity, along with labels indicating whether the sentences are similar.

• Word Embeddings:
  You must have pre-trained word embeddings for the words in your dataset. Word embeddings are dense representations of words in a vector space, which capture the meaning of words and can be used as inputs for neural networks.

• Basic Understanding of Neural Networks:
  A good understanding of the underlying concepts of neural networks is helpful to understand the model, its architecture, and how to train and fine-tune the model.

• Familiarity with Transformer Models:
  Transformer models like BERT, RoBERTa, GPT, etc. are also used in sentence similarity tasks, and they have shown to be very effective, so it would be an added advantage if you are familiar with transformer models.

How Are We Going to Build This?

To build a Sentence Similarity model in Keras, we first import necessary packages such as keras and transformers. The next step is gathering and preparing the data, which can include loading and preprocessing the data. In this project, we will utilize the Keras Custom Data Generator model. After that, we will construct the model and begin training it.

Fine-tuning is an additional step we will perform during this project. Once the training is complete, we will evaluate the model by testing it end-to-end.

Final Output

This is a glimpse of what we are building and what the output looks like. In building sentence similarity, we use a technique called word embeddings. These embeddings map words to high-dimensional vectors that capture the meaning of the words. It is also worth mentioning that the pre-trained language models such as BERT, RoBERTa, GPT-2 etc., are also used in sentence similarity tasks and are very effective.

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Requirements

To follow along with this tutorial, you will need to have the following libraries installed:

• Numpy: Used for efficient numerical computations.

• Pandas: Used for working with data frames.

• Tensorflow: It is the back-end for our transformer models.

• Transformers: A library for using pre-trained transformer models. It is recommended to have the latest version of these libraries installed. You can install them by running the following command on your command prompt/terminal:

Building a Sentence Similarity Finder in Keras

Sentence similarity is determining the degree of similarity between two given sentences. It is a fundamental task in natural language processing and has many practical applications, such as text summarization, question answering, and information retrieval.

The problem can be formulated as follows:

Given two sentences, S1 and S2, and a similarity score function, sim(S1, S2), the task is to find the similarity score between the two sentences. The similarity score should be between 0 and 1, where 0 indicates that the sentences are completely dissimilar and 1 indicates that the sentences are identical.

The function sim(S1, S2) can be based on various similarity measures such as cosine similarity, Jaccard similarity, or a neural network-based similarity measure. The choice of similarity measure depends on the specific application and the available resources.

In this problem, the input is two sentences, and the output is a similarity score between 0 and 1. The goal is to train a model to predict the similarity score for any pair of sentences accurately.

Sample Data

Let's download the sample data. To download the data, you can download the data via the terminal.

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Sample Dataset

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Preprocess the Data

After loading the data, let's do some preprocessing. Firstly, let's remove the missing data from our dataset.

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Distribution of Data

• Training data:
  Training data is a set of input-output pairs used to train a machine learning model.

• Validation Data:
  Validation data is a set of input-output pairs used to evaluate a machine learning model during training, to optimize its performance, and to prevent overfitting.

  Now let's skip the data "-" from the training and validation data.

  After doing that, let's encode the labels.

Custom Dataloader

Let's build the custom data generator for our data. A custom data generator is a user-defined function that generates data for a machine learning model's training, validation, or testing. It allows us to read, process, and feed data to the model flexibly and efficiently, especially when working with large datasets that do not fit in memory. In deep learning libraries like TensorFlow and Keras, custom data generators are typically implemented as Python generators that yield a batch of data on each call. They can be used with the fit method of the model, which will automatically call the generator and feed the data to the model during training.

Overview of the Pre-trained Models That Can Be Leveraged

Now it's time to build the model.

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Now let's create the data pipeline before training the model.

Implementation and Demonstration

As the model, training, and validation data are ready, we will train the model. The training process focuses on the model's upper layers to extract features and enable the model to utilize the representations from the pre-trained model.

It is essential only to carry out this step once the feature extraction model has converged on the new data. Then, the bert_model can be unfrozen and re-trained using a very low learning rate as an additional step. This step can lead to significant improvement by gradually adapting the pre-trained features to the new data.

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Now let's train the fine-tuned model.

After training the model, let's evaluate the model after creating the testing pipeline.

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Testing

Let's write the helper function to make the inference.

Now let's test on some custom samples.

Output:

Output:

What’s Next?

• One of the first things to do after building a model is to evaluate its performance on a held-out dataset. This can help you to understand how well the model can generalize to new data and identify any areas for improvement.
• You must split your dataset into training and testing sets to evaluate the model's performance. You can then use the testing set to evaluate the model's accuracy, precision, recall, and other evaluation metrics.
• Once you have a model that performs well on the testing set, you can deploy it in a production environment. This step is crucial as it allows the model to be used in real-world applications.
• You will need to consider how to serve the model in an efficient and scalable way, such as using a web service or a command-line interface.