48 lines
1.6 KiB
Python
48 lines
1.6 KiB
Python
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import pandas as pd
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from tabulate import tabulate
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from flair.data import Sentence, Token
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from flair.datasets import SentenceDataset
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from flair.models import SequenceTagger
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def conllu2flair(sentences, label=None):
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fsentences = []
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for sentence in sentences:
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fsentence = Sentence()
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for token in sentence:
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ftoken = Token(token['form'])
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if label:
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ftoken.add_tag(label, token[label])
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fsentence.add_token(ftoken)
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fsentences.append(fsentence)
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return SentenceDataset(fsentences)
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def predict(frame_model, sentence):
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csentence = [{'form': word} for word in sentence]
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fsentence = conllu2flair([csentence])[0]
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frame_model.predict(fsentence)
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possible_intents = {}
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for token in fsentence:
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for intent in token.annotation_layers["frame"]:
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if(intent.value in possible_intents):
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possible_intents[intent.value] += intent.score
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else:
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possible_intents[intent.value] = intent.score
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return max(possible_intents)
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frame_model = SequenceTagger.load('frame-model/final-model.pt')
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data = []
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with open('data.tsv') as f:
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lines = f.readlines()
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for line in lines[1:]:
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data.append((line.split("\t")[0], line.split("\t")[1]))
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correct = 0
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for sentence in data:
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predicted_intent = predict(frame_model, sentence[0].split())
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if predicted_intent == sentence[1].replace('\n',''):
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correct+=1
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else:
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print(predicted_intent + " != " + sentence[1].replace('\n',''))
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print(f"{correct/len(data)} {correct}/{len(data)}")
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