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--- |
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language: fr |
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tags: |
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- conversational |
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widget: |
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- text: "bonjour." |
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- text: "mais encore" |
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- text: "est ce que l'argent achete le bonheur?" |
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--- |
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## a dialoggpt model trained on french opensubtitles with custom tokenizer |
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trained with this notebook |
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https://colab.research.google.com/drive/1pfCV3bngAmISNZVfDvBMyEhQKuYw37Rl#scrollTo=AyImj9qZYLRi&uniqifier=3 |
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config from microsoft/DialoGPT-medium |
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dataset generated from 2018 opensubtitle from opus folowing these guidelines |
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https://github.com/PolyAI-LDN/conversational-datasets/tree/master/opensubtitles with this notebook |
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https://colab.research.google.com/drive/1uyh3vJ9nEjqOHI68VD73qxt4olJzODxi#scrollTo=deaacv4XfLMk |
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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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import torch |
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from transformers import AutoTokenizer, AutoModelWithLMHead |
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tokenizer = AutoTokenizer.from_pretrained("cedpsam/chatbot_fr") |
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model = AutoModelWithLMHead.from_pretrained("cedpsam/chatbot_fr") |
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for step in range(6): |
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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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# print(new_user_input_ids) |
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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( |
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bot_input_ids, max_length=1000, |
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pad_token_id=tokenizer.eos_token_id, |
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top_p=0.92, top_k = 50 |
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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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