'How to Build a Conversational AI bot Using Blenderbot' ai aiapplications
#In a jupyter notebook !pip install transformers #In terminal pip install transformersWe are going to install the PyTorch deep learning library because Blenderbot tokenized and Torch tensors and install your specialized version.
#install in jupyter notebook !pip3 install torch==1.9.1+cu111 torchvision==0.10.1+cu111 torchaudio===0.9.1 -f https://download.pytorch.org/whl/torch_stable.html #Install in Terminal pip3 install torch==1.9.1+cu111 torchvision==0.10.1+cu111 torchaudio===0.9.1 -f https://download.pytorch.org/whl/torch_stable.htmlHere we are going to import and download the pre-trained Blenderbot model from Hugging Facefrom transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration#download and setup the model and tokenizer model_name='facebook/blenderbot-400M-distill' tokenizer=BlenderbotTokenizer.from_pretrained model=BlenderbotForConditionalGeneration.from_pretrained, the name of the model begins with the creator of the model and in this case, it's Facebook, it is then followed by the name of the model which isConversing With our ModelFirstly, we are going to put up an utterance, an utterance is like a sentence used to begin the conversation with the conversation agent .Next, we are going to turn the utterance into a token so the model can process it.Above we tokenized our utterance and returned the token as a PyTorch Tensor so it can be processed by the blenderbot model.#generate model results result=model.generateRemember our result response is still in the form of a PyTorch Tensor so now we are going to decode it back to a human-readable form .Once you run the code above you'll see your agent's response.Blenderderbot is a really interesting library, it is able to carry on conversations based on the given context with close similarity to normal human conversation due to the fact that, unlike other conversational agents, it's able to store responses long term, unlike others that have goldfish brains. Due to this, the model takes important information gotten during conversation and stores it in long-term memory so it can then leverage this knowledge in ongoing conversations that may continue for days, weeks, or even months. The knowledge is stored separately for each person it speaks with, which ensures that no new information learned in one conversation is used in another.by
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