babyGPT 1.0.4

An educational module for experimenting with unsupervised learning in large language modeling

Consult the module API page at

https://engineering.purdue.edu/kak/distGPT/babyGPT-1.0.4.html

for all information related to this module, including information related to the latest changes to the code. The page at the URL shown above lists all of the module functionality you can invoke in your own code.

Creating an instance of babyGPT:

        baby_gpt = babyGPT(
                            max_seq_length = max_seq_length,
                            batch_size = batch_size,
                            embedding_size = embedding_size,
                            num_basic_decoders = num_basic_decoders,
                            num_atten_heads = num_atten_heads,
                            optimizer_params = optimizer_params,
                            num_warmup_steps = num_warmup_steps,
                            masking = masking,
                            verify_text_corpus = False,
                            path_saved_model = {"decoder" : "./saved_decoder",
                                                "embedding_generator" : "./saved_embedding_generator",
                                               },
                          )

Since babyGPT calls on TransformerFG for language modeling, you must also construct an instance of that class:

        xformer = baby_gpt.TransformerFG(
                            max_seq_length = max_seq_length,
                            embedding_size = embedding_size,
                            tokenizer_json = tokenizer_json,
                            num_warmup_steps = num_warmup_steps,
                            optimizer_params = optimizer_params,
                  )

Withing the TransformerFG module, it is the MasterDecoder class that is needed for the next word prediction for the purpose of self-supervised learning:

        master_decoder = baby_gpt.MasterDecoderWithMasking(
                            xformer,
                            num_basic_decoders = num_basic_decoders,
                            num_atten_heads = num_atten_heads,
                            masking = masking
                         )


Finally, here is an instance of the dataloader you're going to need:

        dataloader = baby_gpt.ArticleDatasetWithBufferedContext(
                            gpt = baby_gpt,
                            tokenizer_json = tokenizer_json,
                            context_window_size = context_window_size,
                            context_buffer_size = context_buffer_size,
                            articles_dir = articles_dir,
                     )