Natural Language Processing (NLP) solutions for legal contracts have been the preserve of large law firms and other industries (e.g., investment banks), especially those with large amounts of resources, having both the volume and range of legal documents and manpower to label the training data. The findings suggest that it is possible to use a smaller volume of training contacts and still generate results that are within an acceptable range. Our results show that just 120 training contracts trained on a pre-trained language model can generate results that are within 10% of the same model trained on 3.3 times the volume. In conclusion, smaller law firms could benefit from machine learning NLP solutions for clause extraction.