user message updated

README.md

@@ -33,20 +33,14 @@ print(output["generated_text"])
 #### **What I Did**
 I fine-tuned a pre-trained language model using the Hugging Face `transformers` library. The base model was adapted to perform better on specific task by training it on a domain-specific dataset.
 
-#### **Why I Did It**
-The pre-trained model, while powerful, was not optimized for specific domain or task. Fine-tuning was necessary to adapt the model to the specific requirements of [use case or application]. This helps improve the model's performance and ensures more accurate predictions for the intended task.
-
 #### **How I Did It**
-1. **Dataset Preparation**: 
-   - Collected and preprocessed the dataset. This involved tokenization, padding, and formatting the data to ensure compatibility with the model.
-   - Split the dataset into training and validation sets to monitor the model's performance during training.
 
-2. **Fine-Tuning Setup**:
+**Fine-Tuning Setup**:
    - Configured the model training parameters, including the learning rate, batch size, and number of steps.
    - Used `SFTTrainer` from Hugging Face for seamless training with built-in evaluation capabilities.
    - Trained the model for 1 epoch to prevent overfitting, as the dataset was relatively small and hardware resources were limited.
 
-3. **Training Environment**:
+**Training Environment**:
    - The training was performed in Google Colab using a CPU/GPU environment. 
    - Adjusted batch sizes and learning rates to balance between performance and available resources.
 
@@ -61,16 +55,10 @@ The pre-trained model, while powerful, was not optimized for specific domain or
 1. **Use the Model**: 
    - Load the model using the Hugging Face `transformers` library.
    - Tokenize your inputs and pass them to the model for inference.
-
-2. **Adapting to New Tasks**:
    - If your task or domain differs, fine-tune the model further on your dataset.
    - Follow the same process: prepare the dataset, set training configurations, and monitor evaluation metrics.
 
-3. **Understand Limitations**:
-   - Be mindful that the model may not perform well on tasks outside the scope of its fine-tuning dataset.
-   - If you encounter unexpected biases, consider augmenting the training data or further fine-tuning.
-
-4. **Experiment with Parameters**:
+2. **Experiment with Parameters**:
    - If you have access to better hardware, experiment with larger batch sizes or additional epochs to improve results.
    - Use hyperparameter tuning to find the best configuration for your use case.