Best Practices for Fine Tuning

1. Data Quality: Ensure that your training data is accurate and aligned correctly. When fine-tuning a sentiment analysis model, make sure that the sentiment labels are correctly assigned to the corresponding text examples.

   {"review": "The ambiance was wonderful and our waiter was incredibly attentive.", "label": "positive"}
   {"review": "Fantastic experience - the steak was cooked to perfection!", "label": "positive"}
   {"review": "Had to wait over an hour for our food, which was just mediocre.", "label": "negative"}
   {"review": "Very disappointed. The food was cold and the service was rude.", "label": "negative"}
   {"review": "Pasta dish was average, nothing special. But the dessert was delightful.", "label": "positive"}
   {"review": "I loved the vibrant atmosphere and the live music added a great touch.", "label": "positive"}
   {"review": "Terrible experience. The restaurant was too noisy and food was overpriced.", "label": "negative"}
   {"review": "Delicious sushi and the presentation was stunning!", "label": "positive"}

2. Data Balance: When fine-tuning a multi-class classification model, ensure that you have a balanced distribution of examples across different classes. For instance, if you have a dataset for classifying news articles into categories like sports, politics, and entertainment, aim for a similar number of examples for each category.
3. Data Augmentation: To augment your training data, you can apply techniques like data rotation, flipping, or adding noise. For image classification, you can use libraries like imgaug or Albumentations to perform data augmentation on your image dataset.
4. Data Preprocessing: Depending on the task, you may need to preprocess your data. You can tokenize the text data using libraries like spaCy or NLTK, and perform tasks like stemming or lemmatization.
5. Data Split: Split your dataset into training, validation, and testing sets. For example, you can use the train_test_split function from the scikit-learn library to split your data into a 70-15-15 ratio for training, validation, and testing respectively.
6. Model Selection: Choose the appropriate base model for your task. For instance, you can consider using models like BERT, GPT, or RoBERTa, depending on the specific requirements of your project.