AzGPT Pre-Training/Fine-Tuning

Fine-tuning the ai-forever/mGPT-1.3B-azerbaijan model on your extensive Azerbaijani corpus is an excellent way to enhance the model’s capability in Azerbaijani. Since you’re looking to do this in an unsupervised manner, typically this would involve language modeling or a similar task where the model learns to predict the next word or sequence of words based on the previous context. Here’s how you can approach this:

Step 1: Set Up Your Environment

Ensure you have a suitable environment set up for handling a large model like mGPT-1.3B. You will need:

Hardware: Access to powerful GPUs (e.g., NVIDIA V100, A100) as training a 1.3 billion parameter model is resource-intensive.
Software: Python environment with PyTorch and Hugging Face’s Transformers library installed.

pip install transformers torch

Step 2: Prepare Your Dataset

Data Cleaning: Ensure your text data is clean and free of unwanted characters or formatting issues.
Data Format: Format your dataset appropriately. For unsupervised learning, typically your data will be split into chunks (sentences or paragraphs) that the model will use to predict the next token.

Step 3: Load the Pre-trained Model and Tokenizer

You will start by loading the model and its corresponding tokenizer.

from transformers import GPT2LMHeadModel, GPT2Tokenizer

model_name = "ai-forever/mGPT-1.3B-azerbaijan"
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)

Step 4: Tokenize Your Data

Convert your text data into a format suitable for model input.

def tokenize_function(examples):
    return tokenizer(examples['text'], truncation=True, max_length=512, padding="max_length")

# Assuming your dataset is loaded as a Hugging Face dataset
from datasets import load_dataset
dataset = load_dataset('text', data_files={'train': 'path_to_your_dataset.txt'})
tokenized_datasets = dataset.map(tokenize_function, batched=True)

Step 5: Define Training Arguments

Set up the training parameters using Hugging Face’s TrainingArguments.

from transformers import Trainer, TrainingArguments

training_args = TrainingArguments(
    output_dir="./model_outputs",
    per_device_train_batch_size=4,  # Adjust based on GPU memory
    num_train_epochs=3,             # Depends on the size of your dataset and desired training time
    logging_dir='./logs',
    logging_steps=100,
    save_steps=500,
    warmup_steps=500,
    weight_decay=0.01,
    learning_rate=2e-5
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_datasets['train']
)

Step 6: Start Training

Initiate the training process to fine-tune the model on your Azerbaijani text corpus.

trainer.train()

Step 7: Save and Evaluate the Model

After training, save your model and evaluate its performance.

model.save_pretrained("./final_model")

Evaluate the model using qualitative tests (like generating text) or quantitative metrics (like perplexity if you have a validation set).

Conclusion

This approach fine-tunes the ai-forever/mGPT-1.3B-azerbaijan model in an unsupervised manner to improve its performance on Azerbaijani text. It leverages the existing pre-trained capabilities of the model and enhances it with domain-specific knowledge from your text corpus. Ensure to monitor the training for any potential issues, such as overfitting or resource constraints.

For the task of unsupervised fine-tuning a language model like ai-forever/mGPT-1.3B-azerbaijan, the dataset needs to be prepared in a specific format that allows the model to learn from the text effectively. Here’s a detailed breakdown of how you should format your dataset:

Format Overview

The goal is to format your dataset so that each entry (line or paragraph) provides a self-contained piece of text which the model will use to predict the next words. The model essentially learns the structure of the language by trying to predict the next word in the sequence given the previous words.

Detailed Dataset Format

File Type:
- Text Files (.txt): Simple text files are typically used, where each line contains a separate piece of text.
Content Structure:
- Single Sentences or Paragraphs: Each line should ideally be a full sentence or a paragraph. This helps the model learn to predict the end of sentences and handle different sentence structures.
Encoding:
- UTF-8: Ensure the file is encoded in UTF-8 to handle any special characters or symbols in Azerbaijani.

Examples:

Imagine a dataset with simple news snippets, stories, or sentences in Azerbaijani. Here's how you might structure the file:

Bakının mərkəzində yeni park salınır.
Sabah hava necə olacaq?
Azərbaycanın iqtisadiyyatı son illərdə sürətlə inkişaf edir.
Ən yaxşı dostumla kafeə gedəcəyik.
Dünya çempionatında milli komandamız uğurlu çıxış etdi.

Each line is a complete sentence, providing the model with various examples of Azerbaijani syntax and vocabulary.

Using the Dataset for Training

Tokenization: Before training, this text will be tokenized. The tokenizer converts each sentence into a series of tokens (numerical representations), which are then used by the model for training.
Batching: During training, these tokens are batched into groups (determined by the batch_size in your training arguments). The model learns by predicting the next token in each batch.

Visual Example with Illustrator

Imagine you're illustrating this dataset preparation process:

Visual 1: Show a text document icon with Azerbaijani sentences listed (as above).
Visual 2: Illustrate the process of tokenization where sentences are broken down into tokens. Display tokens below each sentence.
Visual 3: Show how these tokens are grouped into batches, preparing them for input into the model.

This visualization can help in understanding how raw text data is transformed into a format suitable for training a language model, with each step designed to teach the model the patterns and structure of the Azerbaijani language.

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