Update README.md

README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+# 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`:
+
+```bash
+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
+Copy code
+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
+Copy code
+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
+Copy code
+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
+Copy code
+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
+Copy code
+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.
+
+