Case Study

project description

  • In this project, we created an AI model which can generate text based on a given input, and the input will be 1-2 sentences.

  • Using this model we can generate a story, information, or content related to a given input.

  • Our model can generate text up to 2-3 paragraphs.

  • We have used the GPT-J model available for fine-tuning and trained this model on a different dataset collected from different places.

  • Moreover, for user-friendliness, we have also created a simple UI along with Flask API. So to get a prediction FrontEnd will call the API of Flask and it will return the output.

Technologies used

  • We created this entire project in Python.

  • To create the model we have used the PyTorch library of python.

  • Moreover, we have also used the huggingface transformer library to get the model architecture.

Difficulties we faced

  • The significant difficulty with this project was the selection of models, which model can be used to generate text with higher coherence and accuracy.

  • We also faced a problem while training the model, as the model is too big to be trained on a normal CPU.


  • For the model, we tested a few different models available for text generations like GPT-2, GPT-Neo, BERT, Roberta, etc. And selected GPT-J as our final product.

  • For the training of the model, we selected google colab pro+, as it provides a high-power GPU for training such a model.


10 months

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