Kwaku/gpt2-finetuned-banking77 was fine tuned on the banking77 dataset, which is "composed of online banking queries annotated with their corresponding intents."

Intended Uses and Limitations

Given the magnitude of the Microsoft DialoGPT-large model, the author resorted to fine-tuning the gpt2 model for the creation of a chatbot. The intent was for the chatbot to emulate a banking customer agent, hence the use of the banking77 dataset. However, when the fine-tuned model was deployed in the chatbot, the results were undesirable. Its responses were inappropriate and unnecessarily long. The last word of its response is repeated numerously, a major glitch in it. The model performs better in text-generation but is prone to generating banking-related text because of the corpus it was trained on.

How to use

You can use this model directly with a pipeline for text generation:

python

>>>from transformers import pipeline

>>> model_name = "Kwaku/gpt2-finetuned-banking77"
>>> generator = pipeline("text-generation", model=model_name)
>>> result = generator("My money is", max_length=15, num_return_sequences=2)
>>> print(result)

[{'generated_text': 'My money is stuck in ATM pending. Please cancel this transaction and refund it'}, {'generated_text': 'My money is missing. How do I get a second card, and how'}]

Limitations and bias

For users who want a diverse text-generator, this model's tendency to generate mostly bank-related text will be a drawback. It also inherits the biases of its parent model, the GPT2.