Bert Job Title classifier gives strange results when trained on a larger dataset
I have attempted to create a BERT Job Title Classifier based off some other examples. Colab can be viewed here:https://colab.research.google.com/drive/1EXKrNIIaVLR9cnpysJCS-NnciksgMoZC?usp=sharingWhen I train the model on a small 1k entry dataset such ashttps://drive.google.com/uc?export=download&id=1Q29nC9Y1x6QGwSiKfu2ie45RG_p-BQ7kI  get expected results from my small test case.programmer: Information & Communication Technology - Engineering - Software
builder: Manufacturing, Transport & Logistics - Machine Operators
mechanic: Trades & Services - Automotive Trades
office assistant: Administration & Office Support - Administrative Assistants
welder: Trades & Services - Welders & BoilermakersHowever, If I train the model on a larger dataset containing 10K entries such ashttps://drive.google.com/uc?export=download&id=1eFMpgiSmhsNoZCBPrwlJlPZ6Lp_Bqw2YMy small test case gives incorrect resultsprogrammer: Administration & Office Support - Administrative Assistants
builder: Administration & Office Support - Administrative Assistants
mechanic: Administration & Office Support - Administrative Assistants
office assistant: Administration & Office Support - Administrative Assistants
welder: Administration & Office Support - Administrative AssistantsSo at this stage I am confused if the issue is in the code or the dataset.I am currently training the model using the sub_classification_id. Should I instead train the model on the parent classification_id, then perform further analysis to determine the sub_classification_id?If anyone could offer any guidance it would be highly appreciated.