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Tagalog DialoGPT

A DialoGPT-medium model fine-tuned on Tagalog conversational data scraped from the web. This model is an output of a research on RoBERTa-based data augmentation for low resource languages. This is the baseline model which did not use any synthetic data in training.

Latest release: July 25, 2021

  • The model is currently only able to respond based on the history of 3 previous utterances before being limited. This is a result of the scarce amount of Tagalog conversations in our dataset.


PEx Conversations Dataset


Here is an example of using beam search for model inference.

for step in range(2): 
    # encode the new user input, add the eos_token and return a tensor in Pytorch
    new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')

    # append the new user input tokens to the chat history
    bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids

    # we limit the generation to 512 tokens, each utterance in training had a maximum of 128 tokens
    chat_history_ids = model.generate(
        bot_input_ids, max_length=512,
    # pretty print last ouput tokens from bot
    print("DialoGPT: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))

Training Script

Fine-tuning script adapted from Spanish DialoGPT

Research by

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Inference API (serverless) has been turned off for this model.

Dataset used to train gabtan99/dialogpt-tagalog-medium

Space using gabtan99/dialogpt-tagalog-medium 1