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--- |
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thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png |
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language: |
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- en |
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license: cc-by-4.0 |
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tags: |
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- conversational |
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- transformers |
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datasets: |
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- AfriWOZ |
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metrics: |
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- perplexity |
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widget: |
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- text: "How I fit chop for here?" |
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--- |
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## DialoGPT_AfriWOZ (Pidgin) |
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This is a fine-tuned model of DialoGPT (small) on the AfriWOZ dataset. It is intended to be used as a conversational system in Nigeria Pidgin English language. |
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The dataset it's trained on is limited in scope, as it covers only certain domains such as restaurants, hotel, taxi, and booking. |
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The perplexity achieved on the validation set is 38.52. |
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* Generation example from an interactive environment: |
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|Role | Response | |
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|------|------------| |
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|User | I hear say restaurant dey here. | |
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|Bot | I dey find restaurant. | |
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|User | Abeg you fit tell me which kind chop dey? | |
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|Bot | I no dey sure right now. | |
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|User | You do well. Thank you. | |
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|Bot | I dey find restauarant. | |
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Please find the information about preprocessing, training and full details of the DialoGPT in the [original DialoGPT repository](https://github.com/microsoft/DialoGPT) |
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The paper for this work can be found on arXiv: [https://arxiv.org/pdf/2204.08083.pdf](https://arxiv.org/pdf/2204.08083.pdf) |
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### How to use |
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Now we are ready to try out how the model works as a chatting partner! |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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import torch |
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tokenizer = AutoTokenizer.from_pretrained("tosin/dialogpt_afriwoz_pidgin") |
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model = AutoModelForCausalLM.from_pretrained("tosin/dialogpt_afriwoz_pidgin") |
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# Let's chat for 5 lines |
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for step in range(5): |
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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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# generated a response while limiting the total chat history to 1000 tokens, |
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chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id) |
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# pretty print last ouput tokens from bot |
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print("DialoGPT_pidgin_Bot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True))) |
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