| # # NER | |
| # Notebook implementation of named entity recognition. | |
| # Adapted from [promptify](https://github.com/promptslab/Promptify/blob/main/promptify/prompts/nlp/templates/ner.jinja). | |
| import json | |
| import minichain | |
| # Prompt to extract NER tags as json | |
| class NERPrompt(minichain.TemplatePrompt): | |
| template_file = "ner.pmpt.tpl" | |
| def parse(self, response, inp): | |
| return json.loads(response) | |
| # Use NER to ask a simple queston. | |
| class TeamPrompt(minichain.Prompt): | |
| def prompt(self, inp): | |
| return "Can you describe these basketball teams? " + \ | |
| " ".join([i["E"] for i in inp if i["T"] =="Team"]) | |
| def parse(self, response, inp): | |
| return response | |
| # Run the system. | |
| with minichain.start_chain("ner") as backend: | |
| ner_prompt = NERPrompt(backend.OpenAI()) | |
| team_prompt = TeamPrompt(backend.OpenAI()) | |
| prompt = ner_prompt.chain(team_prompt) | |
| # results = prompt( | |
| # {"text_input": "An NBA playoff pairing a year ago, the 76ers (39-20) meet the Miami Heat (32-29) for the first time this season on Monday night at home.", | |
| # "labels" : ["Team", "Date"], | |
| # "domain": "Sports" | |
| # } | |
| # ) | |
| # print(results) | |
| gradio = prompt.to_gradio(fields =["text_input", "labels", "domain"], | |
| examples=[["An NBA playoff pairing a year ago, the 76ers (39-20) meet the Miami Heat (32-29) for the first time this season on Monday night at home.", "Team, Date", "Sports"]]) | |
| if __name__ == "__main__": | |
| gradio.launch() | |
| # View prompt examples. | |
| # + tags=["hide_inp"] | |
| # NERPrompt().show( | |
| # { | |
| # "input": "I went to New York", | |
| # "domain": "Travel", | |
| # "labels": ["City"] | |
| # }, | |
| # '[{"T": "City", "E": "New York"}]', | |
| # ) | |
| # # - | |
| # # View log. | |
| # minichain.show_log("ner.log") | |