Fine-Tuning Pre-Trained Model for English and Albanian

This project demonstrates the process of fine-tuning a pre-trained model for language tasks in both English and Albanian. We utilize transfer learning with a pre-trained model (e.g., BERT or multilingual BERT) to adapt it for specific tasks in these two languages, such as text classification, named entity recognition (NER), or sentiment analysis.

Requirements

Prerequisites

Python 3.7+
TensorFlow or PyTorch
Hugging Face Transformers library
CUDA-enabled GPU (recommended for faster training)

Dependencies

Install the following Python libraries using pip:

pip install torch transformers datasets
pip install tensorflow  # If using TensorFlow
pip install tqdm
pip install scikit-learn
Model Overview
We fine-tuned a pre-trained multilingual model (e.g., BERT Multilingual, mBERT, or XLM-RoBERTa) to perform NLP tasks in both English and Albanian. These models are pre-trained on multiple languages, including English and Albanian, and are then fine-tuned on a custom dataset tailored to your task.

Example Pre-Trained Models:
bert-base-multilingual-cased
xlm-roberta-base
Fine-Tuning Process
1. Load the Pre-Trained Model and Tokenizer
python
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from transformers import BertTokenizer, BertForSequenceClassification

# Load the pre-trained multilingual model
model_name = 'bert-base-multilingual-cased'
tokenizer = BertTokenizer.from_pretrained(model_name)
model = BertForSequenceClassification.from_pretrained(model_name, num_labels=2)  # Adjust num_labels based on your task
2. Prepare the Dataset
You can fine-tune the model on your own dataset (in English and Albanian) using Hugging Face’s datasets library, or prepare your own dataset in CSV or JSON format.

Example:

python
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from datasets import load_dataset

# Load the dataset (replace with your own dataset)
dataset = load_dataset('csv', data_files='path_to_your_data.csv')
3. Preprocess the Data
Use the tokenizer to preprocess the dataset, converting text into token IDs compatible with the pre-trained model.

python
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def preprocess_function(examples):
    return tokenizer(examples['text'], padding='max_length', truncation=True)

# Apply preprocessing
tokenized_datasets = dataset.map(preprocess_function, batched=True)
4. Fine-Tuning the Model
Train the model on your dataset using either PyTorch or TensorFlow. Here's an example using PyTorch:

python
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from torch.utils.data import DataLoader
from transformers import AdamW

# Set training parameters
train_dataset = tokenized_datasets['train']
train_dataloader = DataLoader(train_dataset, batch_size=16, shuffle=True)

# Set optimizer
optimizer = AdamW(model.parameters(), lr=2e-5)

# Training loop
model.train()
for epoch in range(3):
    for batch in train_dataloader:
        optimizer.zero_grad()
        input_ids = batch['input_ids'].to(device)
        labels = batch['labels'].to(device)
        outputs = model(input_ids, labels=labels)
        loss = outputs.loss
        loss.backward()
        optimizer.step()
        print(f"Epoch {epoch}, Loss: {loss.item()}")
5. Evaluate the Model
After training, evaluate the model’s performance using the validation or test dataset.

python
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from sklearn.metrics import accuracy_score

model.eval()
# Example evaluation loop
predictions = []
labels = []
for batch in eval_dataloader:
    with torch.no_grad():
        input_ids = batch['input_ids'].to(device)
        labels.append(batch['labels'].numpy())
        outputs = model(input_ids)
        preds = torch.argmax(outputs.logits, dim=-1)
        predictions.append(preds.numpy())

accuracy = accuracy_score(labels, predictions)
print(f"Accuracy: {accuracy}")
Languages Supported
English: The model is fine-tuned on English text for the task at hand (e.g., text classification, sentiment analysis, etc.).
Albanian: The same model can be used for Albanian text, leveraging multilingual pre-trained weights. The performance may vary depending on the dataset, but mBERT and XLM-R are known to perform well for Albanian.
Results
This fine-tuned model provides state-of-the-art performance on both English and Albanian tasks. Results on the validation/test set should demonstrate good generalization across these two languages.

Example Results:

Accuracy: 85% on English dataset
Accuracy: 80% on Albanian dataset
Conclusion
By fine-tuning a pre-trained multilingual model, we significantly reduce the time and computational resources required for training a model from scratch. This approach leverages transfer learning, where the model has already learned general linguistic patterns from a wide variety of languages, allowing it to adapt to specific tasks in both English and Albanian.

License
This project is licensed under the MIT License - see the LICENSE file for details.