/gpt2-geo

The GPT2GeoLMHead project is a natural language processing (NLP) venture aimed at training a Georgian language model head utilizing the powerful GPT-2 architecture.

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GPT2GeoLMHead

🇬🇪 <GPT2GeoLMHead>

Overview:

The GPT2GeoLMHead project is a natural language processing (NLP) venture aimed at training a Georgian language model head utilizing the powerful GPT-2 architecture. This initiative involves the utilization of state-of-the-art transformer-based language models to understand and generate coherent and contextually relevant Georgian text.

Components:

1. Data Collection and Preparation:

  • The project leverages the Wikimedia/Wikipedia dataset for training the language model.
  • The dataset is loaded using the Hugging Face datasets library, focusing on the Georgian language (ka) split from November 1, 2023.

2. Dataset Processing:

  • A custom dataset, GeorgianDataset, is created to preprocess and tokenize the Georgian text data using the ElectraTokenizerFast.
  • The dataset supports dynamic block sizing and includes the necessary special tokens for proper model training.

3. Model Configuration:

  • The GPT-2 model, pretrained on a diverse range of internet text, is utilized as the base architecture.
  • A specific instance of the ElectraTokenizerFast is employed for tokenization in alignment with the model's requirements.
  • Special tokens, including <pad>, <eos>, and <mask>, are added to enhance model understanding and context.

4. Training Loop:

  • The training process is orchestrated by the GPT2GeoLMHead class, which encapsulates the GPT-2 model, tokenizer, and training logic.
  • Training hyperparameters, such as learning rate, batch size, and the number of epochs, are configured in a dedicated Config class.
  • The training loop involves both training and validation phases, utilizing PyTorch's DataLoader for efficient data handling.
  • CrossEntropyLoss is employed as the loss function, and the AdamW optimizer is used for gradient descent.

5. Inference:

  • The model includes an inference method, allowing users to generate Georgian text given a prompt.
  • Various parameters, such as num_beams and temperature, can be adjusted to influence the diversity and creativity of generated text.

6. Model Saving:

  • The project incorporates functionality to save the pretrained GPT-2 model and tokenizer for future use.

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Usage:

  1. Initialization:

    • Create an instance of GPT2GeoLMHead by providing the GPT-2 model and ElectraTokenizerFast.
    • Configure training parameters and dataset size in the Config class.

  2. Training:

    • Call the train method on the GPT2GeoLMHead instance, passing the training and validation datasets.

  3. Inference:

    • Utilize the inference method to generate Georgian text based on a given prompt.

  4. Model Saving:

    • Save the pretrained model and tokenizer using the save_pretrained method.

Conclusion:

The GPT2GeoLMHead project showcases the application of cutting-edge language models for understanding and generating Georgian text. Its modular and object-oriented design facilitates easy extension and integration into various NLP applications. The project stands as a testament to the synergy of transformer architectures and the rich linguistic diversity encapsulated by the Georgian language.