🔸 F-IE-1: Text Classification using NLP

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Building an NLP Pipeline: Text Classification

Foundational Track - Introductory AI Explorations

This project has been selected for expansion into 2024 virtual-internships. Interested? Apply here.


Objective

The principal aim of this project is to construct a Natural Language Processing (NLP) pipeline with a focus on text classification. Students will learn how to convert textual data into numerical representations (using two distinct methods: OpenAI’s embedding APIs and Python libraries), and develop models that perform accurate classifications based on these representations.


Learning Outcomes

By the completion of this project, you will:

  • Comprehend the fundamentals and practical applications of an NLP pipeline, with an emphasis on text classification.
  • Understand the concept and application of embeddings, including popular types like word2vec, GloVe, and BERT.
  • Gain experience using OpenAI’s Embedding APIs.
  • Acquire hands-on skills building text classification models
  • Become proficient in using Accuracy, Precision, Recall, and F1-Score for model evaluation.

Pre-requisite Skills

  • Foundational Python programming
  • Introductory NLP knowledge

Skills Gained

  • Building NLP pipelines
  • Implementing text embeddings
  • Developing text classification systems
  • Evaluating model performance with key metrics

Tools Explored

  • Python libraries like Pandas and Scikit-Learn
  • OpenAI Embedding API
  • BERT and other transformer models
  • Word2Vec and GloVe embeddings
  • PyTorch for deep learning models

Steps and Tasks

At any point during your project, if you find yourself needing assistance, several resources are available to support you:

  • Code Snippets: Code snippets are shared for each step.
  • Code-Along Discussion Category: Join discussions to exchange ideas and resolve issues.
  • STEM-Away Mentorship Category: For paid members, access live mentorship, including forum support and webinars, to enhance your learning experience.

1. Conceptual Understanding

This curated list of resources has been designed to guide you through the journey of building a text classification system. Starting from the very basics of Machine Learning and NLP, you’ll progressively move to more specialized topics like text embeddings, transformer models, and finally, practical applications with PyTorch.

These resources are ordered to ensure a logical progression and comprehensive understanding. Take your time to fully comprehend each topic before moving to the next. Remember, the goal is to build a solid foundation and then apply this knowledge to real-world problems. Let’s get started!

Take your first steps with:

Get familiar with:

Start mastering:


:bulb: You are now ready to begin coding! If you require assistance in setting up the coding environment, please refer to the Appendix. The Appendix provides detailed guidance on establishing your development environment and configuring OpenAI access.


2. Embedding the Data

In this step, you’ll utilize Python and OpenAI’s APIs to convert textual data into significant numerical vectors, a process known as embedding. The data you will employ is derived from the Web Scraping starter project that derived data from Startup Jobs. If you did not participate in that project, you can use this data instead: STEMAway StartUp Job’s Datasets

There are two routes to this:

Route 1: Using OpenAI’s Embedding APIs

OpenAI provides a simple way to generate embeddings via their API. We have shared an example code below. However, to better understand the API usage, please check the official guide for detailed instructions and examples for a range of applications, including recommendation and classification systems.

Click to view basic example of how to use OpenAI API to embed your data 
import openai

openai.api_key = 'your-api-key'

def embed_text(text):
    response = openai.Embed.create(model="text-davinci-002", texts=[text])
    return response['embeddings'][0]['vectors'][0]

# Assume 'data' is a list of your texts
embeddings = [embed_text(text) for text in data]

Route 2: Embeddings using Python Libraries

For the Python route, there are several libraries available, such as Word2Vec (part of Gensim), GloVe, and BERT. You can refer to the following resources for detailed guides:

Click to view a simple code snippet demonstrating how to use BERT for a text classification task

Here’s a simple code snippet demonstrating how to use BERT for a text classification task. This example covers tokenizing sentences, preparing the labels, and performing a basic training step. Remember, this is a very basic example, and real-world applications would require additional steps, like data splitting, multi-epoch training, model performance evaluation, and model saving.

# First, we install the necessary libraries
# !pip install transformers
# !pip install torch

# Import the required libraries
from transformers import BertTokenizer, BertForSequenceClassification
import torch

# Load the pre-trained BERT model and the BERT tokenizer
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

# Assume we have some training data in two lists: sentences (the text data) and labels (the associated labels for each text)
sentences = ['This is the first sentence.', 'This is another sentence.']
labels = [0, 1]  # Suppose it's a binary classification task

# Tokenize the sentences
inputs = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt")

# Specify the labels
inputs['labels'] = torch.tensor(labels).unsqueeze(0)

# Now, we're ready to train the model. This is a simple example, so we'll just do one pass over the data
outputs = model(**inputs)

loss = outputs.loss
loss.backward()

optim = torch.optim.Adam(model.parameters())
optim.step()

# Remember, this is a very simplified example. A real-world application would involve more steps, 
# including splitting your data into a training and validation set, running the training process for more than one epoch, 
# periodically evaluating performance on the validation set, and saving the best model

These embeddings can be used locally on your machine without needing to make requests to an external API. Do note that using these libraries may require a good understanding of NLP and may also require significant computational resources, especially for large datasets.

By the end of this step, you will have transformed your text data into a form that can be input into various machine learning algorithms, setting the stage for building your recommendation and classification systems.

:bulb: Choosing between Python-based libraries and an API like OpenAI’s depends on your specific use case, and each approach provides a unique learning experience.

Learn more about the 2 approaches

Python-based libraries such as Word2Vec, GloVe, and BERT are excellent tools for gaining a deeper understanding of how embeddings work under the hood. Implementing these models from scratch offers invaluable experience and understanding in natural language processing (NLP) and machine learning. You’ll also get to handle challenges in computational resource management and efficiency, as these models can be resource-intensive. This approach can be advantageous if you’re looking for a lot of customization, as these libraries are highly flexible and adaptable.

On the other hand, OpenAI’s API provides a more user-friendly and less computationally intense approach. It’s a great option if you’re new to the field, as it simplifies the process and allows you to quickly move onto the stages of building recommendation and classification systems. The API’s encapsulated nature means that you can access powerful embedding models without needing to understand their inner workings in depth or manage computational resources.

Both paths provide the essential skill of transforming raw text data into numerical form that can be processed by algorithms. Using both approaches in this project allows you to compare their results and gain a balanced and well-rounded understanding of the domain. Having experience with both methods prepares you for a variety of scenarios in your future data science projects, as you can choose the best tool for your specific needs. The hybrid approach also makes you versatile and adaptable in the fast-evolving field of machine learning and data science.


3. Building a Text Classification System

Once your data is embedded, you can start building a a text classification system. This system will categorize the data based on certain criteria (for instance, you could classify job postings by industry or role based on the job description).

  • Preprocessing the Data

You’ll need to preprocess your data to ensure your classification labels are well-defined and uniformly formatted. For example, if you’re classifying job postings by industry, make sure the industry labels are consistent.

  • Choosing and Implementing the Models

    For your first classification models, you’ll be using Support Vector Machines (SVM) and Logistic Regression. Both of these are common models used in text classification, and they work well with text data represented by embeddings.

  • Training the Models

    After implementing the models, you’ll need to train them using your preprocessed data. This involves splitting your data into a training set and a testing set, then fitting your models on the training set.

    Here is a code snippet on how to train a Logistic Regression model 
    from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

# Assume 'embeddings' is a 2D numpy array containing your embeddings and 'labels' is a 1D numpy array containing your labels (0 and 1)
X_train, X_test, y_train, y_test = train_test_split(embeddings, labels, test_size=0.2, random_state=42)

# Create a Logistic Regression classifier
classifier = LogisticRegression()
classifier.fit(X_train, y_train)

# Evaluate the classifier on the test data
print(f"Accuracy: {classifier.score(X_test, y_test)}")

  • Evaluating the Models

    Once your models are trained, you need to evaluate them to see how well they’re performing. Use metrics such as accuracy, precision, recall, and F1 score to evaluate your models. You can refer to this guide to understand these metrics better.

  • Exploring Further

    After working with SVM and Logistic Regression, do some research to implement a third classification model of your choice. Remember not to use pre-trained models for this task. This step will help you explore different options and understand the strengths and weaknesses of various models.

By following these steps, you’ll gain a solid understanding of how to create a text classification system, from data preprocessing to model evaluation. It will also give you a good foundation to explore more complex models and techniques in the future.


Evaluation Process

For a comprehensive understanding of the evaluation process and STEM-Away tacks, please take a moment to review the general details provided here. Familiarizing yourself with this information will ensure a smoother experience throughout the assessment.

For the first part of the evaluation (MCQ), please click on the evaluation button located at the end of the post. Applicants achieving a passing score of 8 out of 10 will be invited to the second round of evaluation.

Advancing to the Second Round:
If you possess the required expertise for an advanced conversation with the AI Evaluator, you may opt to bypass the virtual internships and directly pursue skill certifications.

Evaluation for Virtual-Internships Admissions

  • Start with a Brief Project Overview: Begin by summarizing the project objectives and the key technologies you used (NLP Pipelines, Embeddings, OpenAI API, Machine Learning Models). This sets the context for the discussion.

  • Discuss Pipeline Construction: Explain the process of constructing the NLP pipeline. Discuss any challenges you faced, such as handling large-scale data or dealing with noisy data, and how you addressed these issues.

  • Challenges and Problem-Solving: Present a specific challenge you faced, like identifying key features for embeddings or optimizing model performance. Explain your solution and how it impacted the classification quality and insights. This shows critical thinking and problem-solving skills.

  • Insights from Embedding Techniques: Share an interesting finding from your embedding techniques. For example, “I found that using BERT embeddings provided more contextually relevant classifications compared to traditional methods like word2vec.”

  • Real-world Application: Discuss how you would apply this NLP pipeline in real-world scenarios. Talk about potential applications in content filtering, spam detection, or sentiment analysis and the implications of your findings.

  • Learning and Growth: End by reflecting on your learning journey such as “Working on this project, I gained a deep appreciation for how sophisticated embeddings can capture the nuances of textual data. I also realized the importance of iterative model tuning to enhance classification accuracy.”

  • Ask Questions: Show curiosity by asking the AI mentor questions. For example, “I’m curious, how do large-scale text classification systems handle the integration of real-time data with precomputed embeddings for dynamic classifications?” [/details]

Evaluations for Skill Certifications on the Talent Discovery Platform

  • NLP Pipeline and Completeness:

    • Pipeline Accuracy: Discuss the accuracy and completeness of the NLP pipeline you constructed. Provide examples of how well the pipeline handled the dataset and any areas where it could be improved.
    • Challenges Faced: Describe any significant challenges encountered during the pipeline construction and how you addressed these issues.

  • Embedding Techniques:

    • Embedding Methods: Explain the different embedding methods you used, such as word2vec, GloVe, BERT, or OpenAI Embedding API. Highlight significant findings or challenges you encountered.
    • Learning Curves: Discuss the trends observed during the embedding process, highlighting any substantial discoveries or persistent challenges.

  • Scalability and Practical Applications:

    • Handling Complex Datasets: Describe any challenges you faced with the dataset’s complexity and size. Discuss strategies you employed for managing large-scale data and optimizing your NLP pipeline.
    • Application of Findings: Share how the insights gained from the NLP pipeline could be applied in real-world scenarios, particularly in the domain of text classification.

  • Comparative Analysis and Methodology Evaluation:

    • Methodology Comparison: Compare the methodologies used in constructing and analyzing the NLP pipeline, such as different embedding techniques or machine learning models. Highlight how certain methodologies were more effective in revealing meaningful classifications.
    • Tool Effectiveness: Evaluate the effectiveness of the tools and libraries used, such as OpenAI Embedding API, BERT, or PyTorch for implementing deep learning models.

  • Domain-Specific Considerations:

    • Text-Based Classifications: Discuss the importance of text-based classifications in various applications like content filtering, spam detection, or sentiment analysis. Highlight how your analysis can contribute to enhancing these systems.
    • Related Fields and Tools: Mention other relevant fields such as sentiment analysis, text classification, or natural language understanding, and how similar NLP techniques can be applied. Discuss the use of additional tools like TensorFlow, spaCy, or NLTK for expanding the pipeline’s capabilities.
    • Advanced Techniques and Tools: Share any advanced techniques or tools you explored, such as using transformers for contextual embeddings or incorporating reinforcement learning for dynamic classifications. [/details]

Appendix


Environment Setup

Environment Setup

Before you can begin working on the project, you’ll need to set up your programming environment. If you’re new to Python or need a refresher, here’s a step-by-step guide to get you started:

1. Install Python

Download and install Python from the official Python website: Download Python | Python.org. The latest version as of my training cut-off in September 2021 is Python 3.9, but any version from Python 3.6 and up should work for this project.

2. Install pip

pip is a package manager for Python that we’ll use to install the necessary libraries for this project. If you installed Python from the official website, you should already have pip installed. You can check by typing pip --version in your command line.

If you don’t have pip installed, you can follow the instructions here: Installation - pip documentation v24.1

3. Set up a Virtual Environment

It’s a good practice to create a virtual environment for each of your Python projects. This isolates your project and its dependencies from other projects, which can prevent version conflicts.

Here’s how you can set up a virtual environment:

  • On Windows:
python -m venv myenv
  • On macOS and Linux:
python3 -m venv myenv

Replace myenv with the name you want to give your virtual environment.

To activate the virtual environment, navigate to the directory where you created the environment and run:

  • On Windows:
myenv\Scripts\activate
  • On macOS and Linux:
source myenv/bin/activate

4. Install Necessary Libraries

Once your virtual environment is activated, you can install the necessary Python libraries for this project using pip. For this project, you’ll likely need the following libraries:

  • numpy
  • pandas
  • sklearn
  • nltk
  • tensorflow or pytorch (for BERT embeddings)
  • gensim (for word2vec and GloVe embeddings)

You can install them using the following command:

pip install numpy pandas scikit-learn nltk tensorflow gensim

For PyTorch installation, follow the guide on the official PyTorch website (https://pytorch.org/), as the installation command varies depending on your system and whether you have CUDA available.

With these steps, you should have your programming environment set up and ready to go for this project. Happy coding!


OpenAI Setup

1. Create an OpenAI Account

Go to https://beta.openai.com/signup/ and sign up for an account. You will need to provide some information, including your email address. After you sign up, you should receive a confirmation email. Once your email is confirmed, you can log in to your account.

2. Get Your API Key

Once you’ve logged into your OpenAI account, navigate to the API section of the dashboard (https://beta.openai.com/dashboard/api-keys/). From here, you can generate a new API key.

3. Install OpenAI Python Client

You can install the OpenAI Python client library using pip:

pip install openai

4. Use the API Key in Your Code

Now that you have your API key, you can use it in your Python code to make requests to the OpenAI API. Here’s an example of how you might use it:

import openai

openai.api_key = 'your-api-key'

# Example usage of the API:
response = openai.TextEmbeddings.create(
  texts=["Once upon a time..."]
)

Replace 'your-api-key' with the API key you obtained from the OpenAI dashboard. Be sure to keep your API key confidential, do not publish or share it with anyone, and do not commit it in version control systems.

That’s it! You should now be able to use OpenAI’s Embedding API in your project. Remember to follow OpenAI’s usage policies and guidelines while using their APIs.


Feeling stuck? Click to see a Python code snippet showing how to train a simple text classification model using embeddings 
# Import necessary libraries
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from gensim.models import Word2Vec

# Assume X is your list of sentences (list of lists of words)
# and y is the list of corresponding labels.

# First, let's create word2vec embeddings for our text data.
# We're creating a Word2Vec model and training it on our sentences.
model = Word2Vec(sentences=X, vector_size=100, window=5, min_count=1, workers=4)
model.train(X, total_examples=len(X), epochs=10)

# Now, we'll create sentence vectors by averaging word2vec vectors of all words in a sentence.
X_vectors = []
for sentence in X:
    sentence_vector = np.mean([model.wv[word] for word in sentence], axis=0)
    X_vectors.append(sentence_vector)

# Splitting the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X_vectors, y, test_size=0.2, random_state=42)

# Define the model
lr_model = LogisticRegression()

# Train the model
lr_model.fit(X_train, y_train)

# Evaluate the model
print('Training accuracy:', lr_model.score(X_train, y_train))
print('Testing accuracy:', lr_model.score(X_test, y_test))

More Learning Resources

Decision Trees and Random Forests for Text Classification:

  1. “Decision Trees for Text Classification” by Youssef Obeidi

    • Link: Medium
    • This article provides a practical guide to using decision trees for text classification tasks, including code examples and explanations.

  2. “Random Forest for Text Classification in Python” by Dipanjan Sarkar

Ensemble Methods for Text Classification:

  1. “Ensemble Methods for Text Classification” by Siddhartha Banerjee

    • Link: Medium
    • This article explores different ensemble techniques, such as bagging, boosting, and stacking, for improving text classification performance.

  2. “Kaggle Competition: Ensemble Methods for Text Classification” by Abhishek Thakur

Neural Networks for Text Classification:

  1. “Text Classification with Deep Learning” by Jason Brownlee

  2. “Text Classification With LSTM and GRU Networks” by Valerio Velardo

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Access the Code-Along for this Skill-Builder Project to join discussions, utilize the t3 AI Mentor, and more.

As of June 25th, the Code-Along has been updated to include specific tasks that will serve as your evaluation for Virtual-Internships. These tasks can be used as an alternative to the traditional AI Evaluator process.