Language Classification using TensorFlow

This repository contains a Jupyter Notebook for classifying languages using TensorFlow. The notebook demonstrates the process of building a language classification model that can identify the language of a given text.

Description

The notebook walks through the following steps:

Loading Packages: Importing necessary libraries such as TensorFlow, NumPy, Pandas, and Scikit-learn.

Data Preparation: Loading and preprocessing the dataset, including label encoding and text cleaning.

Model Building: Creating a neural network model using TensorFlow’s Keras API.

Training: Training the model on the dataset.

Evaluation: Evaluating the model’s performance using accuracy and confusion matrix.

Prediction: Making predictions on new text data.

Installation

To run the notebook, you need to have the following packages installed:

• TensorFlow
• NumPy
• Pandas
• Scikit-learn
• Wikipedia-API (optional, for additional data)

You can install these packages using pip:

pip install tensorflow numpy pandas scikit-learn wikipedia-api

Usage

1. Clone the repository:

   git clone https://github.com/cizodevahm/Language-Classification-using-TensorFlow.git

2. Navigate to the repository directory:

   cd Language-Classification-using-TensorFlow

3. Open the Jupyter Notebook:

   jupyter notebook classifier_language_recognition-tensor-flow.ipynb

4. Follow the steps in the notebook to train and evaluate the model.

Example

Here is an example of how to use the model to predict the language of a new text:

new_text = ["tensorflow es una gran herramienta puedes encontrar muchos tutoriales de packt"]
test_text = tok.texts_to_sequences(new_text)
test_text = tf.keras.preprocessing.sequence.pad_sequences(test_text, maxlen=maxlen)
predictions = model.predict(test_text)
print(predictions.argmax())  # Output: 3 (Spanish)

License

This project is licensed under the MIT License.