Master word2vec in Python

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Master word2vec in Python

Table of Contents

  1. Introduction
  2. Installing Gensim Library
  3. Importing Gensim and Pandas
  4. Downloading the Amazon Product Reviews Dataset
  5. Reading the JSON File and Creating a DataFrame
  6. Preprocessing the Review Text
  7. Building a Word2Vec Model
  8. Training the Model
  9. Saving and Loading the Model
  10. Testing the Model
  11. Exercise: Training a Word2Vec Model on Sports and Outdoors Reviews Dataset
  12. Conclusion

Introduction

In this video, we will Continue from where we left off in the previous video where we discussed the theory behind word2vec. In this part, we will be coding using the Gensim library to build a word2vec model. We will be using the Amazon Product Reviews dataset for cell phone accessories to train our model. I have provided an Outline of the entire video to give You an overview of the coding and exercises we will be doing.

Installing Gensim Library

Before we start coding, we need to install the Gensim library. You can easily install it by running the command pip install gensim. If you already have it installed, you can skip this step.

Importing Gensim and Pandas

Once the library is installed, we can import the necessary modules - Gensim and Pandas. Gensim is an NLP library for Python and Pandas is a data manipulation library. We will be using both of these libraries throughout the coding process.

Downloading the Amazon Product Reviews Dataset

Next, we will download the Amazon Product Reviews dataset for cell phone accessories. This dataset contains product reviews specifically for cell phone accessories. You can click on the provided link to download the dataset.

Reading the JSON File and Creating a DataFrame

After downloading the dataset, we will Read the JSON file using Pandas. Pandas has built-in support for reading JSON files, making our task easier. We will Create a DataFrame from the JSON file, which will allow us to manipulate and analyze the data.

Preprocessing the Review Text

Before training our word2vec model, we need to preprocess the review text. This involves removing stop words, converting words to lowercase, removing punctuation marks, and other preprocessing tasks. Gensim provides a simple_preprocess function that can be used to preprocess the text easily.

Building a Word2Vec Model

With the preprocessed review text, we can now build our word2vec model. We will initialize a Word2Vec object from the Gensim library, specifying the window size, min_count, and workers parameters. The window size determines the number of words before and after the target word to consider as Context, while min_count determines the minimum number of times a word should appear in the dataset to be considered. The workers parameter determines the number of CPU Threads to use for training the model.

Training the Model

Once the model is initialized, we can train it using the train() method. We will pass the preprocessed review text and other parameters like the total_examples and epochs. The total_examples parameter specifies the total number of examples in our dataset, while epochs determines the number of times the model should iterate over the dataset.

Saving and Loading the Model

After training the model, we can save it to a file for future use. This allows us to use the pre-trained model in other projects. We will use the save() method of the Word2Vec model to save it to a file. Additionally, we can also load a pre-trained model using the load() method.

Testing the Model

To test the trained model, we can use the most_similar() method to find similar words for a given word. We can also calculate the Cosine similarity between two words using the similarity() method. This allows us to explore the semantic relationships between words in our trained model.

Exercise: Training a Word2Vec Model on Sports and Outdoors Reviews Dataset

As an exercise, you can train a word2vec model on the Sports and Outdoors reviews dataset. This dataset is also provided, and you can follow a similar process to train the model. Make sure to try it out on your own before checking the provided solution to test your understanding.

Conclusion

In conclusion, the Gensim library provides an easy-to-use interface for training word2vec models. By leveraging the power of word embeddings, we can capture the semantic relationships between words and use them for various NLP tasks.

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