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# # 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")
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