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--- |
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language: |
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- en |
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thumbnail: |
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tags: |
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- gpt2 |
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- conversational |
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license: cc-by-4.0 |
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widget: |
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- text: Hello there |
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context: 'Gandalf' |
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--- |
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# GPT2 Persona Chatbot based on Movie Characters |
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Model used for https://www.metayazar.com/chatbot |
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GPT2 Small Trained on movie scripts (especially Sci-fi) |
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Usual HF api will not work see HF Spaces for demo usage https://huggingface.co/spaces/gorkemgoknar/moviechatbot |
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This work is based on Persona Chatbot originally done by Hugging Face team (https://medium.com/huggingface/how-to-build-a-state-of-the-art-conversational-ai-with-transfer-learning-2d818ac26313) |
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For cleaning movie scripts I also provide cleaner code |
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https://github.com/gorkemgoknar/moviescriptcleaner |
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Example persona how to: |
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https://gist.github.com/gorkemgoknar/ae29bf9d14fa814e6a64d0e57a4a4ed7 |
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Tried a AI job interview over some characters here, details on this post |
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https://www.linkedin.com/pulse/ai-goes-job-interview-g%C3%B6rkem-g%C3%B6knar/ |
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For obvious reasons I cannot share raw personafile but you can check above gist for example how to create it. |
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A working "full" demo can be seen in https://www.metayazar.com/chatbot |
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For Turkish version (with limited training) https://www.metayazar.com/chatbot_tr |
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Due to double LM head standart hugging face interface will not work. But if you follow huggingface tutorial should be same. |
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Except each persona is encoded as "My name is XXXX" |
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Use model, tokenizer and parameters within a class and call in below functions to trigger model. |
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Some of the available personas: |
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| Macleod | Moran | Brenda | Ramirez | Peter Parker | Quentin Beck | Andy |
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| Red | Norton | Willard | Chief | Chef | Kilgore | Kurtz | Westley | Buttercup |
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| Vizzini | Fezzik | Inigo | Man In Black | Taylor | Zira | Zaius | Cornelius |
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| Bud | Lindsey | Hippy | Erin | Ed | George | Donna | Trinity | Agent Smith |
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| Morpheus | Neo | Tank | Meryl | Truman | Marlon | Christof | Stromboli | Bumstead |
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| Schreber | Walker | Korben | Cornelius | Loc Rhod | Anakin | Obi-Wan | Palpatine |
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| Padme | Superman | Luthor | Dude | Walter | Donny | Maude | General | Starkiller |
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| Indiana | Willie | Short Round | John | Sarah | Terminator | Miller | Sarge | Reiben |
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| Jackson | Upham | Chuckie | Will | Lambeau | Sean | Skylar | Saavik | Spock |
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| Kirk | Bones | Khan | Kirk | Spock | Sybok | Scotty | Bourne | Pamela | Abbott |
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```python |
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def get_answer(self, input_text, personality, history, params=None): |
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##Check length of history (to save 1 computation!) |
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if len(history)>0: |
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#mostly it will be empty list so need a length check for performance |
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#would do string check also but just assume it is list of list of strings, as not public |
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new_hist = [] |
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for ele in history: |
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new_hist.append( self.tokenizer.encode(ele) ) |
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history = new_hist.copy() |
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history.append(self.tokenizer.encode(input_text)) |
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with torch.no_grad(): |
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out_ids = self.sample_sequence(personality, history, self.tokenizer, self.model, params=params) |
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history.append(out_ids) |
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history = history[-(2*self.parameters['max_history']+1):] |
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out_text = self.tokenizer.decode(out_ids, skip_special_tokens=True) |
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#print(out_text) |
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history_decoded = [] |
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for ele in history: |
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history_decoded.append(self.tokenizer.decode(ele)) |
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return out_text, history_decoded, self.parameters |
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``` |
