<p>In this project, we worked with the Skip-Gram model, widely used for creating vector representations in NLP. The word embedding in this case refers to the process of transforming words into numbers that can be processed by the computer efficiently. This way the model can show how words are connected semantically and contextually. Consequently, this model type is helpful for, for instance, search engines, recommendations, and text categorization.</p>

Everyone understands the fact that a language is made up of words. Moreover, combining them appropriately is essential in many intricate activities such as natural language processing (NLP) and machine learning.

Skip Gram Model Python Implementation for Word Embeddings

<h2>Project Overview</h2><p>This project aims to understand the Skip-Gram model and explores how it can be employed to produce meaning and relationships of words in numerical values called word embeddings. The procedure begins by cleaning and preprocessing the given input text to get rid of any irrelevant data and the data is ready for analysis. We then proceed to build a vocabulary that is geared towards training a neural net whereby the model attempts to guess the context words given a center word.</p><p>The model then saves the embeddings generated for some future use and their effectiveness is assessed by looking for analogous terms and measuring the distances of word pairs. For this purpose, we employed t-SNE, which is a dimensionality reduction technique with a particular focus on two- dimensional spaces to facilitate the perception of such embeddings. This suggests that such visual representation helps us understand how closely the words that are related are located to each other.</p><p>The interest contains the focus on nearly practical tasks, for instance, searching for keywords to find appropriate ones or dealing with datasets by using word topology. This also combines the technological advancements of machine learning and the enhanced techniques in illustrations, thus providing an understanding of how words are related in a text, which makes it useful for many NLP activities.</p><h2>Prerequisites</h2><p>Learners must develop some skills before undertaking this project. Here’s what you should ideally know:</p><ul><li>Python version 3.7 or higher installed on your system.   </li><li>Understanding of basic knowledge of <strong>Python</strong> for data analysis and manipulation  </li><li>Knowledge of libraries such as <strong>NLTK, Scikit-learn, Pandas, NumPy, and Matplotlib</strong> is necessary.  </li><li>Jupyter Notebook, VScode, or a Python-compatible IDE.   </li><li>You must have experience with the PyTorch framework.  </li><li>The ability to understand how to use text preprocessing techniques is essential.</li></ul><h2>Approach</h2><p>The project started with the gathering and cleaning of the text data whereby noise was ensured to be taken out by normalizing and tokenizing the text. Then a vocabulary was created by indexing each word and word counts were done with negative sampling support. Using this vocabulary, positive center-context word pairs with a given window were created while negative samples were created to enhance the learning of the model. </p><p><strong>Skip-gram</strong> model was instantiated in <strong>PyTorch</strong> where embedding layers and weight matrices were configured to capture semantic relations. The model underwent training where the Adam optimization technique was utilized in training and batches of data with monitoring of loss to ensure all training was progressive. After the training, all the embedding dimensions and weights were exported for further research. To translate the embeddings to concrete terms, <strong>t-SNE</strong> was employed to reduce the dimensions after which the word embeddings were plotted to depict the clusters and how the words relate with each other semantically. Distant and similarity measurements were made on the embeddings to assess their performance in representing word relationships.</p><h2>Workflow and Methodology</h2><h3>Workflow</h3><ul><li><strong>Data Preparation</strong>: Acquire and prepare raw text by eliminating noise and standardizing the text for uniformity in processing.  </li><li><strong>Vocabulary Designing</strong>: Design a vocabulary with an index for words in it and prepare word counts for negative sampling.  </li><li><strong>Sample Generation:</strong> Create a positive center–context word pair and negative samples using a context sliding window.  </li><li><strong>Model Development:</strong> Implement the PyTorch Skip-Gram model with the help of Adam optimizer and keep track of the changes in loss function during training.  </li><li><strong>Storage of Embedding:</strong> Save the word embeddings and the associated weights after training for later use in various tasks.  </li><li><strong>Visualization</strong>: Use t-SNE to visualize the learned embeddings in two dimensions and show the relationship of words in a two-dimensional figure.  </li><li><strong>Validation</strong>: Validate the embeddings generated by computing the similarity between words and their distance from each other with their meaning. </li></ul><h3>Methodology</h3><ul><li>Preprocessed the text to clean and tokenize for effective input to the model.  </li><li>Built a Skip-Gram neural network with embedding layers to capture semantic word relationships.  </li><li>Trained the model on center-context pairs while incorporating negative sampling for enhanced learning.  </li><li>Applied t-SNE to reduce embedding dimensions for visualizing semantic groupings of words.  </li><li>Assessed embeddings through distance and similarity metrics to ensure meaningful word representations.</li></ul><h2>Data Collection and Preparation</h2><h4>Data Collection:3</h4><p>In this project, we collected the dataset from a public repository. If you are looking to work on a real-world problem, you can get these kinds of datasets from publicly available repositories such as Kaggle, UCI Machine Learning Repository, or company-specific data. We will provide the dataset in this project so that you can work on the same dataset.</p><h4>Data Preparation Workflow:</h4><ul><li>Load the text data in DataFrame for further processing.  </li><li>Clean data by removing missing values so that data is complete.  </li><li>Lowercase text for uniformity.  </li><li>Using regular expressions remove special characters, numbers, and repeated sequences.  </li><li>Reformatting multiple spaces to have clean up for formatting.  </li><li>NLTK's word_tokenize will tokenize text into words  </li><li>Store tokenized data as a pickle file for some other future use.</li></ul>

<h2>Code explanation</h2><p>Here’s what is happening under the hood. Let’s go through it step by step:</p><h3>Step 1:</h3><h4>Mount Google Drive</h4><p>Mount your Google Drive to access and save datasets, models, and other resources.</p>

<h4>Library Import</h4><p>The necessary libraries for processing language, managing data, performing machine learning, and creating visualizations are imported into this code. It consists of deep learning libraries such as PyTorch, Text processing libraries such as <strong>nltk</strong>, various metrics libraries such as <strong>sklearn</strong>, and graph plotting libraries such as <strong>matplotlib</strong>. All these tools assist in performing certain tasks such as evaluating the performance of an algorithm, training the model, and analyzing the text.</p>

<h4>Acquiring the Punkt Sentence Tokenizer from the NLTK Library</h4><p>This code is for downloading the punkt tokenizer data available in the NLTK library. It facilitates the process by breaking down the text into sentences and parsing the sentences into words for all the natural language processing operations.</p>

<h4>Saving and Loading Pickle Files</h4><p>These functions preserve and reproduce Python objects with the help of the pickle module. save_file saves an object to the disk while load_file pulls an object from a pickle file.</p>

<h3>STEP 2:</h3><h4>Loading Data and Checking Shape:</h4><p>This code loads the CSV file. After loading the dataset, it prints the dataset’s shape to check the number of rows and columns. The %time magic command in the notebook records the time taken to perform the task.</p>

<h4>Dropping the Missing Data and Checking the Shape of the Data</h4><p>This code deletes all the rows that do not have any value in the Consumer complaint narrative column. After that, the shape of the updated dataset is displayed, portraying the number of rows and columns remaining.</p>

<h4>Extracting  the text column and showing the sample data</h4><p>The code extracts the Consumer complaint narrative column to input_text for further analysis. Afterward, it uses head() to show some of the dataset’s first few rows.</p>

<h4>Converting Text to Lowercase</h4><p>This script utilizes a list comprehension method to transform all the alphabets present in input_text in the list to the small case for ease of processing the text. The progress in the operations is shown by tqdm.</p>

<h4>Eliminating Special Character from a String</h4><p>This piece of code eliminates special characters except for words, numbers, and spaces in the input_text with the regular expression. This helps in cleaning the text and makes it ready for further processing.</p>

<h4>Removing the digits in a text</h4><p>With the use of regular expressions, this code removes all numeric characters from the input_text, hence allowing for the processing of only the text content.</p>

<h4>Eliminating Consecutive 'x' Characters</h4><p>The purpose of this particular code is to erase all sequences in input_text that consist of two or more consecutive 'x' characters using regular expression thereby enhancing the cleanup of the text.</p>

<h4>Removing Extra Spaces</h4><p>This code removes multiple consecutive spaces in the input_text and replaces them with a single space, ensuring cleaner text formatting.</p>

<h4>Tokenizing Texts to Words</h4><p>Here, for the first 100 data in the input_text, we apply NLTK's word_tokenize. Each entry turns into a list of tokens, which are further used for processing.</p>

<h4>Saving the Tokenized data</h4><p>This code saves the tokenized data into a file located in the designated path for tokens utilizing the later-defined <strong>save_file</strong> function.</p>

<h3>STEP 3:</h3><h3>Configuring Parameters for Text Processing</h3><p>This particular code describes the following three parameters:</p><ul><li>k: The number of top k words or top k neighbors;   </li><li>t: a threshold value such as that used in the filtering of infrequent words;   </li><li>context_window: The dimension of the text analysis context window. </li></ul><p>Such parameters are normally employed in natural language processing applications for word embedding or context understanding.</p>

<h4>The SkipGramDataset Class for Preparing Data for Skip-Gram Models</h4><p>This class implements a training dataset for skip-gram models. It produces pairs of center and context words, performs negative sampling, and maps words to indices. It also stores vocabulary mappings for later retrieval.</p>

<h3>STEP 4:</h3><h4>Defining Embedding Size</h4><p>This code sets the embedding_size to 64, specifying the size of the word embedding vectors for the Skip-Gram model.</p>

<h4>SkipGram Neural Network</h4><p>Here’s a code implementation of the Skip Gram model in PyTorch. It performs word embeddings, assesses similarity for the center and context, and saves all the embeddings and parameters.</p>

<h4>Tuning the Hyperparameters for the Skip-Gram Mode</h4><p>The hyperparameters for the Skip-Gram model are set in this code especially identifying the embedding size, learning rate, batch size, number of epochs, and context window size. The model outputs are specified with an output path presented herein.</p>

<h4>Setting Device for Computation</h4><p>This code sets the device to GPU if available. Otherwise, it uses the CPU for computations.</p>

<h4>Training Function for Skip-Gram Model</h4><p>The Skip-Gram model is trained with a dataloader, criterion, and optimizer. It calculates loss and updates model weights, implements early stopping when there is no improvement of loss after 5 epochs in a row. The model's embeddings and weights are saved once after the training process is finished.</p>

<h3>STEP 5:</h3><h4>Initializing SkipGramDataset object</h4><p>In this code, a SkipGramDataset object will be created from tokenized input data. It specifies context window, negative sample threshold, top-k negatives, saving vocab and other processed files output path, etc.</p>

<h4>Implementing DataLoader for Skip-Gram Dataset</h4><p>In this code, a DataLoader for the SkipGramDataset is created that takes care of batching, shuffling and efficient loading of data whenever the model is being trained.</p>

<h4>Initializing SkipGram Model</h4><p>This code creates a SkipGram model instance with the vocabulary size and embedding size from the dataset.</p>

<h4>Loss Function And Optimizer Definition</h4><p>In this code, we set the negative log-likelihood loss (NLLLoss ) as the objective and also define the Adam optimizer with a learning rate of 0.01 for training the Skip-Gram model.</p>

<h4>Training the Skip-Gram Model</h4><p>We are training the Skip-Gram model by invoking the train_sg function with the provided dataloader, model, loss criterion, optimizer, device, and number of epochs.</p>

<h4>Loading Word-to-Index Mapping</h4><p>The code loads the pre-compiled word2index pickle file which provides a mapping of words to their respective index values.</p>

<h4>Retrieving Word Index</h4><p>This code fetches the index of the word "payments" from the word2idx dictionary.</p>

<h4>Loading Word Embeddings</h4><p>This code loads the saved word embeddings from a pickle file </p>

<h4>Obtaining Word Embedding via its Index</h4><p>In this example, the code implements the embedding model and fetches the respective word embedding vector of index 83.</p>

<h4>Loading and Transposing Weights</h4><p>This code loads the embeddings and weights from previously saved files. The weights are reshaped or transposed for further utilization purposes with W2 being the last variable and its final shape is shown using W2.shape.</p>

<h3>STEP 6:</h3><h4>Making a 3D Scatter Graph</h4><p>This piece of code produces some random 3D data and represents it as a scatter plot through Matplotlib. The illustrative diagram incorporates labeled axes (X, Y, Z), a title, and the points of interest in 3D space.</p>

<h4>Finding Similar Words Using Word Embeddings</h4><p>This implementation determines the n most cosine similar words to the given word. It calculates similarity scores for all words available in the vocabulary and gives the most similar top N words. In case the word does not exist in the dictionary it also alerts the user.</p>

<h4>Calculating Euclidean Distance Between Word Embeddings</h4><p>This function calculates the Euclidean distance between the embeddings of two target words. Initially, it ensures that both words are available in the vocabulary, fetches the embedding of the two words, and then computes the distance. The example illustrates the distance between “payment” and “credit”.</p>

<h4>Utilizing t-SNE to visualize embeddings.</h4><p>This script employs t-SNE for the projection of high-dimensional word embedding features into a two-dimensional plane for ease of presentation. The projections of the embeddings are shown as points thereby providing a depiction of the spatial relationships that exist between various words in the embedding space.</p>

<h4>Visual Representation of Word Embeddings using t-SNE with Color Coding</h4><p>This code makes use of t-SNE for dimensionality reduction of word embeddings and applies color to each of them. The technique produces a scatter plot in which each word embedding is depicted as a point in a two-dimensional space making it easier to comprehend the connections between words.</p>

<h4>t-SNE Visualization of a Random Sample of Word Embeddings</h4><p>This segment of code visually depicts the results achieved by t-SNE using 200 randomly selected word embeddings, after reducing their dimensionality to two dimensions. Thus, the embedded words are plotted on a scatter graph and each word is identified by its respective point on the graph.</p>

<h2>Conclusion</h2><p>This project was able to successfully carry out the skip-gram model of word representation in a way that makes sense semantically. Through the processes of effective data preparation, neural network training, and dimensionality reduction techniques such as <strong>t-SNE</strong>, the project showcased how embeddings can be used to spatially and mathematically present the relationships words have with each other. The resulting embeddings were also tested with similarity and distance measurements which proved to be reliable and useful for <strong>natural language processing</strong>. This technique however has many practical uses, especially in <strong>search engines</strong>, r<strong>ecommendation system</strong>s, and <strong>text categorization</strong>. To show skillful integration of complex machine learning tools that can be easily understood, the role of word representation or <strong>embeddings</strong> in <strong>NLP</strong> tasks is quite advanced.</p><h2>Challenges New Coders Might Face</h2><ul><li><p><strong><em>Challenge</em></strong>:  <strong>Handling noisy or unstructured text data.</strong><br /><strong><em>Solution</em></strong>: Utilize text cleaning methods, which may include the exclusion of special symbols, figures, and extra spaces.  </p></li><li><p><strong><em>Challenge</em></strong>:  <strong>Preprocessing Large Text Data</strong><br /><strong>*Solution***</strong>:** Enhance text cleaning processes by employing better libraries such as NLTK and adopting batch processing for the data.</p></li><li><p><strong><em>Challenge</em></strong>: <strong>Curse of Dimensionality in the high dimensional text datasets affecting clustering and classification results.</strong><br /><strong>*Solution***</strong>:** Use TF-IDF vectorization and reduction techniques like (PCA) to control dimensionality.  </p></li><li><p><strong><em>Challenge</em></strong>: <strong>Inaccessibility of GPU</strong><br /><strong><em>Solution</em></strong>: For debugging or initial testing, consider using smaller datasets or incorporating GPU-based cloud platforms for quick turnaround times.</p></li><li><p><strong><em>Challenge</em></strong>: <strong>Training Time is High due to the Large Vocabulary Size</strong><br /><strong><em>Solution</em></strong>: Use subsampling techniques or restrict vocabulary to use only the N most frequent words for efficiency purposes.</p></li></ul><h2>Frequently Asked Questions (FAQs)</h2><p><strong>Question 1: In the realm of natural languages, what is the Skip-Gram model</strong><br /><strong>Answer</strong>: The Skip-Gram Model is a neural architecture that seeks to create word embeddings by predicting context words given a center word to understand how the words relate to one another in textual content.</p><p><strong>Question 2: Why is word embedding so significant in NLP projects?</strong><br /><strong>Answer</strong>: Word embeddings turn words into numbers in vector shape, such that the models can capture the meanings of words to the assigned tasks text classification, sentiment analysis, recommendation systems, and so forth.</p><p><strong>Question 3: In what way does negative sampling enhance the training of Skip-Gram models as hypotheses?</strong><br /><strong>Answer:</strong> The negative sampling alleviates the computational burden by having a model that can tell apart the relevant context words from the irrelevant ones.</p><p><strong>Question 4: What are the preprocessing stages of Skip-Gram projects?</strong><br /><strong>Answer:</strong> These include text cleaning and normalization, tokenization, and other processes to remove noise, such as special characters and numbers, for uniformity of the input data.</p><p><strong>Question 5: What libraries are needed to implement the Skip-Gram model?</strong><br /><strong>Answer:</strong> Building and training the model cannot be done without Pytorch whilst NLTK, NumPy, and Matplotlib are useful for preprocessing, and numerical and graphical manipulations respectively.</p>

Skip Gram Model Python Implementation for Word Embeddings

Code Explanation

Code explanation

Step 1:

Mount Google Drive

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