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--- |
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tags: |
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- conversational |
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- tagalog |
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- filipino |
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language: |
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- tl |
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inference: false |
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datasets: |
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- gabtan99/pex-conversations |
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--- |
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# Tagalog DialoGPT |
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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. |
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# Latest release: July 25, 2021 |
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* 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. |
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# Dataset |
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[PEx Conversations Dataset](https://huggingface.co/datasets/gabtan99/pex-conversations) |
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# Usage |
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Here is an example of using beam search for model inference. |
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``` |
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for step in range(2): |
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# encode the new user input, add the eos_token and return a tensor in Pytorch |
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new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt') |
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# append the new user input tokens to the chat history |
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bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids |
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# we limit the generation to 512 tokens, each utterance in training had a maximum of 128 tokens |
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chat_history_ids = model.generate( |
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bot_input_ids, max_length=512, |
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pad_token_id=tokenizer.eos_token_id, |
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num_beams=5, |
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no_repeat_ngram_size=3 |
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) |
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# pretty print last ouput tokens from bot |
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print("DialoGPT: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True))) |
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``` |
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# Training Script |
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[Fine-tuning script adapted from Spanish DialoGPT](https://colab.research.google.com/github/ncoop57/i-am-a-nerd/blob/master/_notebooks/2020-05-12-chatbot-part-1.ipynb) |
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# Research by |
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* [tyadrianpaule](https://huggingface.co/tyadrianpaule) |
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* [schuylerng](https://huggingface.co/schuylerng) |
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* [dcl127](https://huggingface.co/dcl127) |
