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--- |
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thumbnail: https://images-ext-2.discordapp.net/external/IaDAOIgiVKpnDGgsqAsVEW5jgwIHprFc3dSmlW3U0Ro/%3Fsize%3D4096/https/cdn.discordapp.com/avatars/931226824753700934/51db9904887a38dca03238f9b3479594.png |
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tags: |
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- conversational |
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license: mit |
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--- |
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An generative AI made using [microsoft/DialoGPT-small](https://huggingface.co/microsoft/DialoGPT-small). |
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# AEONA |
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Note the AI is still learning so expect very frequent updates! |
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Soon a sample AIML project and a API will be released! |
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# Free API with the AIML backend! |
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[API](https://rapidapi.com/[email protected]/api/aeona2/) |
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## Goals |
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The goal is to create an AI which will work with AIML in order to create the most human like AI. |
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#### Why not an AI on its own? |
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For AI it is not possible (realistically) to learn about the user and store data on them, when compared to an AIML which can even execute code! |
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The goal of the AI is to generate responses where the AIML fails. |
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Hence the goals becomes to make an AI which has a wide variety of knowledge, yet be as small as possible! |
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So we use 3 dataset:- |
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1. [Movielines](https://www.kaggle.com/Cornell-University/movie-dialog-corpus) The movie lines promote longer and more thought out responses but it can be very random. About 200k lines! |
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2. [Discord Messages](https://www.kaggle.com/jef1056/discord-data) The messages are on a wide variety of topics filtered and removed spam which makes the AI highly random but gives it a very random response to every days questions! about 120 million messages! |
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3. Custom dataset scrapped from my messages, These messages are very narrow teaching this dataset and sending a random reply will make the AI say sorry loads of time! |
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## Training |
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The Discord Messages Dataset simply dwarfs the other datasets, Hence the data sets are repeated. |
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This leads to them covering each others issues! |
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## Usage |
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Example: |
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```python |
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from transformers import AutoTokenizer, AutoModelWithLMHead |
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tokenizer = AutoTokenizer.from_pretrained("deepparag/DumBot") |
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model = AutoModelWithLMHead.from_pretrained("deepparag/DumBot") |
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# Let's chat for 4 lines |
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for step in range(4): |
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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=200, |
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pad_token_id=tokenizer.eos_token_id, |
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no_repeat_ngram_size=4, |
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do_sample=True, |
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top_k=100, |
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top_p=0.7, |
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temperature=0.8 |
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) |
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# pretty print last ouput tokens from bot |
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print("DumBot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True))) |
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``` |
