_assessment

Code performs text-to-text generation using a Seq2Seq model. It starts by gathering the dataset, converting it into a user-friendly Pandas DataFrame, and tidying up the 'output' column for better processing. Then, the data is thoughtfully divided into training, validation, and test sets for effective learning. Essential libraries are installed and updated, ensuring the necessary tools are readily available. The code then delicately tokenizes the input data, crafting a foundation for the Seq2Seq model and its tokenizer. Training parameters, such as batch size and learning rate, are carefully set to guide the learning process. The model undergoes training on the provided dataset and is gently tested on the validation set to assess its performance. Lastly, the fruits of this learning journey – the trained model and its accompanying tokenizer – are safely stored in the secure confines of Google Drive, ready to be utilized for various text generation tasks. Also for the next task implemented the chat with csv for getting insights from the data and provide an assistance to the data scients in the field of EDA and getting basic idea of the and about the dataset