language: eng
datasets:
- banking77
GPT2 Fine-tuned KO
This is a fine-tuned version of the GPT2 model. It's best suited for text-generation.
Model Description
gpt2-finetuned-ko 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 hugeness 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, unnecessarily long and repetitive. The model performs better in text-generation but is prone to generate baking-related texted because of the corpus it was trained on.
How to use
You can use this model directly with a pipeline for text generation:
>>>from transformers import pipeline
>>> model_name = "Kwaku/gpt2-finetuned-ko"
>>> 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.