Master Text Classification in Python with Langchain

Text classification is a vital task in natural language processing (NLP) that involves categorizing text into predefined classes. In this tutorial, you'll learn how to master text classification using Langchain, a powerful Python library. We'll cover the importance of text classification and guide you through the process of implementing it effectively.

Table of Contents

Introduction to Text Classification

Text classification is the process of assigning predefined categories (or labels) to a given text based on its content. Some common applications include:

  • Sentiment analysis (positive, negative, or neutral)
  • Spam detection (spam or not spam)
  • Topic labeling (e.g., sports, politics, technology)

By automating text classification, businesses can reduce manual work, improve efficiency, and make better decisions based on data insights.

What is Langchain?

Langchain is a Python library that simplifies the process of text classification. It provides a high-level interface for training, evaluating, and deploying text classification models. With Langchain, you can easily create powerful and accurate models without worrying about the underlying complexities of NLP and machine learning.

Installation and Setup

To install Langchain, simply run the following command:

pip install langchain

Now that you've installed Langchain, let's import the necessary modules and prepare the dataset for our text classification task.

Preparing Your Data

For this tutorial, we'll use the 20 Newsgroups dataset, a popular dataset for text classification. It contains approximately 20,000 newsgroup posts, evenly distributed across 20 different categories.

First, let's import the dependencies:

import numpy as np
from langchain import LangchainClassifier, LangchainVectorizer
from sklearn.datasets import fetch_20newsgroups
from sklearn.model_selection import train_test_split

Next, let's load the dataset and split it into training and testing sets:

newsgroups = fetch_20newsgroups(subset='all', remove=('headers', 'footers', 'quotes'))
X_train, X_test, y_train, y_test = train_test_split(,, test_size=0.2, random_state=42)

Training a Text Classification Model

Now that our data is ready, let's create a LangchainVectorizer to convert the raw text data into a numerical format:

vectorizer = LangchainVectorizer(max_features=10000)
X_train_vec = vectorizer.fit_transform(X_train)
X_test_vec = vectorizer.transform(X_test)

Next, create a LangchainClassifier and train the model using the training data:

classifier = LangchainClassifier(), y_train)

Evaluating Model Performance

After training the model, let's evaluate its performance on the test set:

accuracy = classifier.score(X_test_vec, y_test)
print(f"Test accuracy: {accuracy * 100:.2f}%")

This will output the accuracy of your model, which should be around 70% or higher. Keep in mind that this is just a simple example, and your model's performance will vary depending on the data and parameters you choose.

Improving Your Model

To improve your model's performance, you can try:

  • Changing the vectorizer's parameters (e.g., increasing max_features)
  • Tuning the classifier's hyperparameters (e.g., adjusting the learning rate or batch size)
  • Using more advanced techniques, such as deep learning models or ensemble methods

Remember to experiment with different approaches and always validate your changes using the test set.


In this tutorial, you've learned how to master text classification using Langchain, a powerful Python library. We covered the importance of text classification, how to prepare your data, train a model, evaluate its performance, and improve it.

With Langchain, you can easily create accurate and efficient text classification models, enabling you to harness the power of NLP and machine learning for your applications.

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