This repository contains a transliteration model that converts Hindi text into English using a sequence-to-sequence deep learning model. The model is trained on a dataset of pairs of Hindi and English words and can generate the corresponding English transliteration for a given Hindi word.
The dataset used for training the transliteration model consists of a collection of Hindi words and their corresponding English transliterations. Each word pair is used to train the model to learn the mapping between the Hindi characters and the corresponding English characters. The dataset should be preprocessed and split into training and validation sets before training the model.
The transliteration model is built using a sequence-to-sequence architecture with an encoder-decoder framework. The encoder is responsible for encoding the input Hindi sequence, while the decoder generates the corresponding English transliteration. The model consists of recurrent neural network (RNN) layers, such as LSTM or GRU, which are commonly used for sequence modeling tasks.
The model is trained using the training dataset, where the input sequences are the Hindi words and the target sequences are the English transliterations. The training process involves feeding the input sequences into the model, comparing the generated output with the target sequences, and adjusting the model's parameters to minimize the loss. The training is typically performed using mini-batch gradient descent and backpropagation through time.
The model's performance is evaluated using a separate validation dataset. The evaluation metrics include loss and accuracy, which measure the model's ability to generate accurate transliterations. The validation dataset is used to assess the model's generalization and to tune hyperparameters, such as the learning rate or the size of the hidden layers.
Once the model is trained, it can be used to generate English transliterations for new Hindi words. The input Hindi word is passed through the trained model, and the decoder generates the corresponding English transliteration character by character. The inference process involves encoding the input sequence with the trained encoder and decoding it with the trained decoder. The generated output is the predicted English transliteration.
To use the transliteration model, follow these steps:
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Prepare the dataset: Preprocess the dataset of Hindi-English word pairs and split it into training and validation sets.
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Train the model: Use the training dataset to train the transliteration model. Adjust the hyperparameters and experiment with different architectures, if necessary.
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Evaluate the model: Measure the performance of the trained model using the validation dataset. Monitor the loss and accuracy to assess the model's quality.
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Save the trained model: Save the trained model's parameters and architecture to a file (e.g.,
model.h5). -
Load the model: Load the trained model from the saved file for future usage.
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Predict transliterations: Use the loaded model to predict English transliterations for new Hindi words. Pass the Hindi word through the model and obtain the generated transliteration.
The implementation of the transliteration model requires the following dependencies:
- Python
- Keras
- NumPy
- Tensorflow
- Pandas
Please ensure that these dependencies are installed before running the code.
This project is licensed under the MIT License.
