How to Use Latent Language Models (LLMs) for Text Classification and Clustering
This tutorial will guide you through the process of using Latent Language Models (LLMs) for text classification and clustering tasks. LLMs are a powerful tool for natural language processing (NLP) that can help you analyze and categorize text data.
Prerequisites
To follow this tutorial, you should have a basic understanding of NLP concepts and be familiar with Python programming language. You will also need the following libraries installed:
- Hugging Face Transformers:
pip install transformers - Scikit-learn:
pip install scikit-learn
Introduction to LLMs
Latent Language Models (LLMs) are based on unsupervised learning techniques that aim to discover the underlying structure within a set of documents. They learn representations of words and documents that capture the semantics and context of the text.
The most commonly used LLM is the Gensim Word2Vec model, which learns word embeddings from a large corpus of text. The word embeddings are dense vectors that represent the words in a semantic space. These word embeddings can be used as features for downstream tasks like classification and clustering.
Text Classification with LLMs
Text classification is the process of assigning predefined categories or labels to a piece of text. LLMs can be used to build classifiers that automatically classify documents based on their content.
Data Preparation
To demonstrate text classification with LLMs, let’s first prepare a dataset. You can use any labeled dataset of your choice. In this tutorial, we will use the 20 Newsgroups dataset, which consists of 20,000 news articles from different categories.
Start by downloading and importing the necessary libraries:
import numpy as np
from sklearn.datasets import fetch_20newsgroups
# Fetch the dataset
categories = ['sci.med', 'sci.space', 'rec.sport.baseball', 'rec.sport.hockey']
data = fetch_20newsgroups(categories=categories, subset='all', shuffle=True, random_state=42)
Now, let’s preprocess the text data before training the LLM. We will use the CountVectorizer from scikit-learn to convert the raw text into a matrix of word counts.
from sklearn.feature_extraction.text import CountVectorizer
# Create a count vectorizer
vectorizer = CountVectorizer(stop_words='english')
# Fit and transform the text data
X = vectorizer.fit_transform(data.data)
y = data.target
# Convert the sparse matrix to a dense matrix
X = X.toarray()
Training the LLM
With the data prepared, we can now train the LLM. In this tutorial, we will use the Word2Vec model from the Gensim library.
Start by installing the Gensim library:
pip install gensim
Now, let’s train the Word2Vec model using the preprocessed text data:
from gensim.models import Word2Vec
# Train the Word2Vec model
model = Word2Vec(sentences=data.data, vector_size=300, window=5, min_count=3, sg=1)
In the code above, we pass the data.data list to the Word2Vec model, which contains the preprocessed text data. We set the vector_size parameter to 300, which defines the dimensionality of the word embeddings. The window parameter determines the maximum distance between the current and predicted word within a sentence. The min_count parameter specifies the minimum frequency of a word to be included in the vocabulary. Finally, the sg parameter signals the use of the Skip-Gram algorithm.
Text Classification with LLM
To perform text classification with LLM, we need to represent each document as a fixed-length feature vector using the word embeddings generated by the Word2Vec model. One simple approach is to compute the average of the word embeddings for all words in the document.
def document_embedding(document, model):
word_embeddings = []
for word in document.split():
if word in model.wv:
word_embeddings.append(model.wv[word])
if len(word_embeddings) == 0:
return np.zeros(model.vector_size)
else:
return np.mean(word_embeddings, axis=0)
# Compute the document embeddings
X_embedded = np.array([document_embedding(doc, model) for doc in data.data])
Now, we can use the document embeddings as input to train a classifier. In this tutorial, we will use the Support Vector Machine (SVM) classifier from scikit-learn.
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X_embedded, y, test_size=0.2, random_state=42)
# Initialize the SVM classifier
classifier = SVC()
# Train the classifier
classifier.fit(X_train, y_train)
# Make predictions on the test set
y_pred = classifier.predict(X_test)
# Calculate the accuracy of the classifier
accuracy = accuracy_score(y_test, y_pred)
With the trained classifier, you can now make predictions on new, unseen documents and evaluate its accuracy.
Text Clustering with LLMs
Text clustering, also known as text segmentation or text categorization, is the process of grouping similar documents together based on their content. LLMs can be used to build clusters that group related documents.
Data Preparation
To demonstrate text clustering with LLMs, let’s prepare another dataset. Again, you can use any dataset of your choice, but for this tutorial, we will use the 20 Newsgroups dataset.
Start by downloading and importing the necessary libraries:
from sklearn.datasets import fetch_20newsgroups
# Fetch the dataset
categories = ['sci.med', 'sci.space', 'rec.sport.baseball', 'rec.sport.hockey']
data = fetch_20newsgroups(categories=categories, subset='all', shuffle=True, random_state=42)
Preprocess the text data by converting it into a matrix of word frequencies using the TfidfVectorizer from scikit-learn:
from sklearn.feature_extraction.text import TfidfVectorizer
# Create a TF-IDF vectorizer
vectorizer = TfidfVectorizer(stop_words='english')
# Fit and transform the text data
X = vectorizer.fit_transform(data.data)
y = data.target
# Convert the sparse matrix to a dense matrix
X = X.toarray()
Training the LLM
With the data prepared, we can now train the LLM. In this tutorial, we will use the Word2Vec model from the Gensim library, similar to the previous example.
from gensim.models import Word2Vec
# Train the Word2Vec model
model = Word2Vec(sentences=data.data, vector_size=300, window=5, min_count=3, sg=1)
Text Clustering with LLM
To perform text clustering with LLM, we will use the K-Means algorithm on the document embeddings generated by the Word2Vec model.
from sklearn.cluster import KMeans
# Initialize the K-Means clustering algorithm
num_clusters = len(categories)
kmeans = KMeans(n_clusters=num_clusters)
# Perform clustering on the document embeddings
kmeans.fit(X_embedded)
# Get the cluster assignments for each document
y_pred = kmeans.labels_
We use num_clusters equal to the number of categories in the dataset to assign each document to a specific cluster. The labels_ attribute of the KMeans object contains the predicted cluster assignments.
Evaluating the Clustering Results
To evaluate the clustering results, we can compare the predicted clusters to the ground truth labels of the dataset using various metrics such as Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI).
from sklearn.metrics import adjusted_rand_score, normalized_mutual_info_score
# Compute the evaluation metrics
ari = adjusted_rand_score(y, y_pred)
nmi = normalized_mutual_info_score(y, y_pred)
# Print the evaluation metrics
print(f"Adjusted Rand Index: {ari}")
print(f"Normalized Mutual Information: {nmi}")
The adjusted_rand_score() and normalized_mutual_info_score() functions from the sklearn.metrics module can be used to compute ARI and NMI, respectively.
Conclusion
In this tutorial, you learned how to use Latent Language Models (LLMs) for text classification and clustering tasks. You saw how to preprocess the text data, train the LLM, and perform classification and clustering using the obtained document embeddings. LLMs are powerful tools for NLP and can be used to gain insights and extract meaningful information from text data.