Here is a general outline of the steps you could take to preprocess data from PDF files and train a language model like GPT-3 Here is a general outline of the steps you could take to preprocess data from PDF files and train a language model like GPT-3 to generate responses:
First, you will need to extract the text from the PDF files. There are several libraries that you can use to do this, such as PyPDF2 or pdfminer.
Once you have extracted the text, you will need to clean and preprocess it. This may include tasks such as lowercasing, removing punctuation, and stemming or lemmatizing the words.
Next, you will need to split the text into individual training examples. For instance, you might want to use a sliding window approach, where you take sequences of consecutive words as input and the following word as the output.
Once you have your training examples, you can use them to train a language model like GPT-3. This will typically involve loading the data into a PyTorch or TensorFlow dataset, defining the model architecture, and training the model using an optimizer like Adam.
After training the model, you can use it to generate responses by providing it with an input prompt and asking it to predict the next word in the sequence. You can then use this predicted word as the input for the next iteration, and so on, until you have generated the desired number of words or reached a stopping condition.
Notes: mkdir foldes befor executing the script
pdf_dir = 'pdf_files'
data_dir = 'preprocessed_data'