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# Neural Network-Based Language Model for Next Token Prediction
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## Overview
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This project implements a neural network-based language model designed for next-token prediction using two languages: English and
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## Table of Contents
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- [Installation](#installation)
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- [Usage](#usage)
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- [Model Architecture](#model-architecture)
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- [Training](#training)
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- [Text Generation](#text-generation)
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- [Results](#results)
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- [License](#license)
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## Installation
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To run this project, you need to have Python installed along with the following libraries:
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pip install torch numpy pandas huggingface_hub
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Usage
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Clone this repository or download the model files.
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Use the following code to load the model and generate text:
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python
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Copy code
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from model import YourModelClass # Import your model class
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model = YourModelClass.load_from_checkpoint('path/to/your/model.pt')
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# Generate text
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Training
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The model was trained using datasets from:
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English: [Description of the dataset]
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Hyperparameters
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Learning Rate
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Batch Size
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Epochs
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Text Generation
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The model can generate text in both English and Assigned Language
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Results
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The training curves for both loss and validation loss are provided in the submission.
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The model's performance is evaluated based on the generated text quality and perplexity score during training.
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# Neural Network-Based Language Model for Next Token Prediction
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+
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## Overview
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This project implements a neural network-based language model designed for next-token prediction using two languages: English and Icelandic. The model is built without the use of transformer or encoder-decoder architectures, focusing instead on traditional neural network techniques.
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## Table of Contents
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- [Installation](#installation)
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- [Usage](#usage)
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- [Model Architecture](#model-architecture)
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- [Training](#training)
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- [Text Generation](#text-generation)
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- [Results](#results)
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- [License](#license)
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## Installation
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+
To run this project, you need to have Python installed along with the following libraries:
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+
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pip install torch numpy pandas huggingface_hub
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+
Usage
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Clone this repository or download the model files.
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+
Use the following code to load the model and generate text:
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python
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+
Copy code
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+
from model import YourModelClass # Import your model class
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model = YourModelClass.load_from_checkpoint('path/to/your/model.pt')
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+
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# Generate text
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+
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+
Training
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The model was trained using datasets from:
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+
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English: [Description of the dataset]
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Icelandic
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Hyperparameters
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+
Learning Rate
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+
Batch Size
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+
Epochs
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+
Text Generation
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+
The model can generate text in both English and Assigned Language
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+
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+
Results
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The training curves for both loss and validation loss are provided in the submission.
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The model's performance is evaluated based on the generated text quality and perplexity score during training.
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