Certainly! Here's a sample README (read.md) for the provided code:

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Text Analysis and Classification with Transformers

Text analysis and classification tasks using the Hugging Face Transformers library. The code covers a wide range of tasks, including dataset loading, preprocessing, tokenization, model embeddings, dimensionality reduction, and text classification.

Getting Started

These instructions will help you set up and run the code on your local machine.

Prerequisites

Make sure you have the following libraries and tools installed:

• Python 3.x
• Hugging Face Transformers
• PyTorch
• scikit-learn
• UMAP
• Pandas
• Matplotlib
• NumPy

You can install these libraries using pip:

pip install transformers torch scikit-learn umap-learn pandas matplotlib numpy

Running the Code

1. Clone this repository to your local machine:

git clone https://github.com/emmanuelalo52/transformers-text-analysis.git
cd transformers-text-analysis

2. Run the Python script text_analysis.py:

python text_analysis.py

The script will walk through various steps for text analysis, tokenization, embeddings, classification, and visualization.

Code Overview

Here's a brief overview of what the code does:

1. Dataset Loading:

   • Imports necessary libraries.
   • Lists available datasets from the Hugging Face hub.
   • Loads the 'emotion' dataset.

2. Data Exploration:

   • Displays dataset information.
   • Accesses and explores dataset columns and features.
   • Converts the dataset to a Pandas DataFrame for better readability.

3. Text Preprocessing:

   • Maps integer labels to their corresponding emotion names.
   • Creates a bar chart to visualize the frequency of emotions.
   • Adds a new column to count the number of words in each tweet and creates a boxplot to visualize word count distribution.

4. Text Tokenization and Encoding:

   • Tokenizes text using a pre-trained tokenizer.
   • Converts tokens back to text.
   • Extracts model input names and tokenizes the entire dataset.

5. Text Embeddings:

   • Loads a pre-trained model for text classification.
   • Extracts hidden states for each text sample in the dataset.
   • Performs dimensionality reduction using UMAP.
   • Visualizes text embeddings using hexbin plots.

6. Text Classification:

   • Trains a logistic regression classifier and evaluates its accuracy.
   • Computes the F1 score.
   • Trains a Support Vector Machine (SVM) classifier and visualizes a confusion matrix.