Finalne poprawki
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@ -1,15 +1,15 @@
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print("Script to automatically append data to data/dialog.conllu")
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print("Start typing now. Press Ctrl+C to stop.")
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while True:
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with open("data/train_dialog.conllu", "a") as f:
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text = input("Text: ")
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act = input("Intent: ")
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slots = text.split(" ")
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f.write(
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f"\n# text: {text}\n# intent: {act}\n# slots:\n"
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)
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for i, slot in enumerate(slots):
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label = input(f"{i}/{slot} label: ")
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f.write(f"{i+1}\t{slot}\t{act}\t{label}\n")
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print("---")
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print("Script to automatically append data to data/dialog.conllu")
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print("Start typing now. Press Ctrl+C to stop.")
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while True:
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with open("data/train_dialog.conllu", "a") as f:
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text = input("Text: ")
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act = input("Intent: ")
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slots = text.split(" ")
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f.write(
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f"\n# text: {text}\n# intent: {act}\n# slots:\n"
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)
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for i, slot in enumerate(slots):
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label = input(f"{i}/{slot} label: ")
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f.write(f"{i+1}\t{slot}\t{act}\t{label}\n")
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print("---")
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File diff suppressed because it is too large
Load Diff
@ -332,11 +332,11 @@
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3 w request/menu NoLabel
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4 ofercie request/menu NoLabel
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# text: chciałbym 3 pizze, hawajskie duże
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# text: chciałbym trzy pizze, hawajskie duże
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# intent: inform/order
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# slots:
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1 chciałbym inform/order NoLabel
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2 3 inform/order B-quantity
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2 trzy inform/order B-quantity
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3 pizze, inform/order B-food
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4 hawajskie inform/order B-pizza
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5 duże inform/order B-size
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@ -585,11 +585,11 @@
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4 tuna inform/order B-pizza
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5 XL inform/order B-size
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# text: wezmę 3 pizze tuna, średnią, dużą i bardzo dużą
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# text: wezmę 3x pizze tuna, średnią, dużą i bardzo dużą
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# intent: inform/order
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# slots:
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1 wezmę inform/order NoLabel
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2 3 inform/order B-quantity
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2 3x inform/order B-quantity
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3 pizze inform/order B-food
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4 tuna, inform/order B-pizza
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5 średnią, inform/order B-size
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@ -825,6 +825,14 @@
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1 jakie request/ingredients NoLabel
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2 składniki request/ingredients NoLabel
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# text: co jest na pizzy
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# intent: request/ingredients
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# slots:
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1 co request/ingredients NoLabel
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2 jest request/ingredients NoLabel
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3 na request/ingredients NoLabel
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4 pizzy request/ingredients NoLabel
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# text: jakie są napoje
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# intent: request/drinks
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# slots:
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@ -850,3 +858,54 @@
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2 macie request/drinks NoLabel
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3 do request/drinks NoLabel
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4 picia request/drinks NoLabel
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# text: czy są dostępne jakieś sosy?
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# intent: request/sauce
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# slots:
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1 czy request/sauce NoLabel
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2 są request/sauce NoLabel
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3 dostępne request/sauce NoLabel
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4 jakieś request/sauce NoLabel
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5 sosy? request/sauce NoLabel
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# text: Grzegorz Pieczarski
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# intent: inform/name
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# slots:
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1 Grzegorz inform/name B-name
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2 Pieczarski inform/name I-name
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# text: Sergiusz Kaczmarek
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# intent: inform/name
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# slots:
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1 Sergiusz inform/name B-name
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2 Kaczmarek inform/name I-name
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# text: jaki koszt dowozu
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# intent: request/delivery-price
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# slots:
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1 jaki request/delivery-price NoLabel
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2 koszt request/delivery-price NoLabel
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3 dowozu request/delivery-price NoLabel
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# text: jakie sosy w menu?
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# intent: request/sauce
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# slots:
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1 jakie request/sauce NoLabel
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2 sosy request/sauce NoLabel
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3 w request/sauce NoLabel
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4 menu? request/sauce NoLabel
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# text: Napój pepsi i cola
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# intent: inform/order
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# slots:
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1 Napój inform/order NoLabel
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2 pepsi inform/order B-drink
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3 i inform/order NoLabel
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4 cola inform/order B-drink
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# text: woda i sok
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# intent: inform/order
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# slots:
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1 woda inform/order B-drink
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2 i inform/order NoLabel
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3 sok inform/order B-drink
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106
evaluate.py
106
evaluate.py
@ -1,45 +1,63 @@
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import re
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import os
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import pandas as pd
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import numpy as np
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from nlu_utils import predict_multiple
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from flair.models import SequenceTagger
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def __parse_acts(acts):
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acts_split = acts.split('&')
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remove_slot_regex = "[\(\[].*?[\)\]]"
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return set(re.sub(remove_slot_regex, "", act) for act in acts_split)
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def __parse_predictions(predictions):
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return set(prediction.split('/')[0] for prediction in predictions)
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# Exploratory tests
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frame_model = SequenceTagger.load('frame-model-prod/best-model.pt')
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# slot_model = SequenceTagger.load('slot-model-prod/final-model.pt')
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total_acts = 0
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act_correct_predictions = 0
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slot_correct_predictions = 0
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for file_name in os.listdir('data'):
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if file_name.split('.')[-1] != 'tsv':
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continue
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df = pd.read_csv(f'data/{file_name}', sep='\t', names=['kto', 'treść', 'akt'])
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df = df[df.kto == 'user']
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all_data = np.array(df)
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for row in all_data:
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sentence = row[1]
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acts = __parse_acts(row[2])
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predictions_raw = predict_multiple(frame_model, sentence.split(), 'frame')
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predictions = __parse_predictions(predictions_raw)
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for act in acts:
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total_acts += 1
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if act in predictions:
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act_correct_predictions += 1
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import re
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import os
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import pandas as pd
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import numpy as np
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from nlu_utils import predict_multiple
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from flair.models import SequenceTagger
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from conllu import parse_incr
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from flair.data import Corpus
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from nlu_utils import conllu2flair, nolabel2o
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# Frame model evaluation
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frame_model = SequenceTagger.load('frame-model-prod/best-model.pt')
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with open('data/test_dialog_46.conllu', encoding='utf-8') as trainfile:
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testset = list(parse_incr(trainfile, fields=['id', 'form', 'frame', 'slot'], field_parsers={}))
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corpus = Corpus(test=conllu2flair(testset, "frame"))
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result = frame_model.evaluate(corpus.test, mini_batch_size=1, gold_label_type="frame")
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print(result.detailed_results)
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# Slot model evaluation
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slot_model = SequenceTagger.load('slot-model-prod/best-model.pt')
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with open('data/test_dialog_46.conllu', encoding='utf-8') as trainfile:
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testset = list(parse_incr(trainfile, fields=['id', 'form', 'frame', 'slot'], field_parsers={'slot': nolabel2o}))
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corpus = Corpus(test=conllu2flair(testset, "slot"))
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result = slot_model.evaluate(corpus.test, mini_batch_size=8, gold_label_type="slot")
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print(result.detailed_results)
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# Custom evaluation
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def __parse_acts(acts):
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acts_split = acts.split('&')
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remove_slot_regex = "[\(\[].*?[\)\]]"
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return set(re.sub(remove_slot_regex, "", act) for act in acts_split)
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def __parse_predictions(predictions):
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return set(prediction.split('/')[0] for prediction in predictions)
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total_acts = 0
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act_correct_predictions = 0
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slot_correct_predictions = 0
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for file_name in os.listdir('data'):
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if file_name.split('.')[-1] != 'tsv':
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continue
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df = pd.read_csv(f'data/{file_name}', sep='\t', names=['kto', 'treść', 'akt'])
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df = df[df.kto == 'user']
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all_data = np.array(df)
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for row in all_data:
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sentence = row[1]
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acts = __parse_acts(row[2])
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predictions_raw = predict_multiple(frame_model, sentence.split(), 'frame')
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predictions = __parse_predictions(predictions_raw)
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for act in acts:
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total_acts += 1
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if act in predictions:
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act_correct_predictions += 1
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print(f"Accuracy - predicting acts: {(act_correct_predictions / total_acts)*100} ({act_correct_predictions}/{total_acts})")
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58
nlu_tests.py
58
nlu_tests.py
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from flair.models import SequenceTagger
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from nlu_utils import predict_single, predict_multiple, predict_and_annotate
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# Exploratory tests
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frame_model = SequenceTagger.load('frame-model/best-model.pt')
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tests = [
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'chciałbym zamówić pizzę',
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'na godzinę 12',
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'prosiłbym o pizzę z pieczarkami',
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'to wszystko, jaka cena?',
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'ile kosztuje pizza',
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'do widzenia',
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'tak',
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'nie dziękuję',
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'dodatkowy ser',
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'pizzę barcelona bez cebuli',
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]
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# print("=== Exploratory tests - frame model ===")
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for test in tests:
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print(f"Sentence: {test}")
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print(f"Single prediction: {predict_single(frame_model, test.split(), 'frame')}")
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print(f"Multiple predictions: {predict_multiple(frame_model, test.split(), 'frame')}")
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print(f"Annotated sentence: {predict_and_annotate(frame_model, test.split(), 'frame')}")
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print("=== Exploratory tests - slot model ===")
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slot_model = SequenceTagger.load('slot-model/final-model.pt')
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for test in tests:
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print(f"Sentence: {test}")
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from flair.models import SequenceTagger
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from nlu_utils import predict_single, predict_multiple, predict_and_annotate
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# Exploratory tests
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frame_model = SequenceTagger.load('frame-model/best-model.pt')
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tests = [
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'chciałbym zamówić pizzę',
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'na godzinę 12',
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'prosiłbym o pizzę z pieczarkami',
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'to wszystko, jaka cena?',
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'ile kosztuje pizza',
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'do widzenia',
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'tak',
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'nie dziękuję',
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'dodatkowy ser',
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'pizzę barcelona bez cebuli',
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]
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# print("=== Exploratory tests - frame model ===")
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for test in tests:
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print(f"Sentence: {test}")
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print(f"Single prediction: {predict_single(frame_model, test.split(), 'frame')}")
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print(f"Multiple predictions: {predict_multiple(frame_model, test.split(), 'frame')}")
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print(f"Annotated sentence: {predict_and_annotate(frame_model, test.split(), 'frame')}")
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print("=== Exploratory tests - slot model ===")
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slot_model = SequenceTagger.load('slot-model/final-model.pt')
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for test in tests:
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print(f"Sentence: {test}")
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print(f"Prediction: {predict_and_annotate(slot_model, test.split(), 'slot')}")
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88
nlu_train.py
88
nlu_train.py
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from conllu import parse_incr
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from flair.data import Corpus
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from flair.embeddings import StackedEmbeddings
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from flair.embeddings import WordEmbeddings
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from flair.embeddings import CharacterEmbeddings
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from flair.embeddings import FlairEmbeddings
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from flair.models import SequenceTagger
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from flair.trainers import ModelTrainer
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from nlu_utils import conllu2flair, nolabel2o
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import random
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import torch
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random.seed(42)
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torch.manual_seed(42)
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if torch.cuda.is_available():
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torch.cuda.manual_seed(0)
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torch.cuda.manual_seed_all(0)
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torch.backends.cudnn.enabled = False
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torch.backends.cudnn.benchmark = False
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torch.backends.cudnn.deterministic = True
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def train_model(label_type, field_parsers = {}):
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with open('data/train_dialog.conllu', encoding='utf-8') as trainfile:
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trainset = list(parse_incr(trainfile, fields=['id', 'form', 'frame', 'slot'], field_parsers=field_parsers))
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corpus = Corpus(train=conllu2flair(trainset, label_type), test=conllu2flair(trainset, label_type))
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label_dictionary = corpus.make_label_dictionary(label_type=label_type)
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embedding_types = [
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WordEmbeddings('pl'),
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FlairEmbeddings('pl-forward'),
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FlairEmbeddings('pl-backward'),
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CharacterEmbeddings(),
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]
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embeddings = StackedEmbeddings(embeddings=embedding_types)
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tagger = SequenceTagger(hidden_size=256, embeddings=embeddings, tag_dictionary=label_dictionary, tag_type=label_type, use_crf=True, tag_format="BIO")
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frame_trainer = ModelTrainer(tagger, corpus)
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frame_trainer.train(f'{label_type}-model', learning_rate=0.1, mini_batch_size=32, max_epochs=75, train_with_dev=False)
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if __name__ == '__main__':
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train_model("frame")
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train_model('slot', field_parsers={'slot': nolabel2o})
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from conllu import parse_incr
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from flair.data import Corpus
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from flair.embeddings import StackedEmbeddings
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from flair.embeddings import WordEmbeddings
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from flair.embeddings import CharacterEmbeddings
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from flair.embeddings import FlairEmbeddings
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from flair.models import SequenceTagger
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from flair.trainers import ModelTrainer
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from nlu_utils import conllu2flair, nolabel2o
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import torch
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if torch.cuda.is_available():
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torch.backends.cudnn.enabled = False
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torch.backends.cudnn.benchmark = False
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torch.backends.cudnn.deterministic = True
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def train_model(label_type, field_parsers = {}):
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with open('data/train_dialog.conllu', encoding='utf-8') as f:
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trainset = list(parse_incr(f, fields=['id', 'form', 'frame', 'slot'], field_parsers=field_parsers))
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with open('data/test_dialog_46.conllu', encoding='utf-8') as f:
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testset = list(parse_incr(f, fields=['id', 'form', 'frame', 'slot'], field_parsers=field_parsers))
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breakpoint()
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corpus = Corpus(train=conllu2flair(trainset, label_type), test=conllu2flair(testset, label_type))
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label_dictionary = corpus.make_label_dictionary(label_type=label_type)
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embedding_types = [
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WordEmbeddings('pl'),
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FlairEmbeddings('pl-forward'),
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FlairEmbeddings('pl-backward'),
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CharacterEmbeddings(),
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]
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embeddings = StackedEmbeddings(embeddings=embedding_types)
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tagger = SequenceTagger(hidden_size=256, embeddings=embeddings, tag_dictionary=label_dictionary, tag_type=label_type, use_crf=True, tag_format="BIO")
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frame_trainer = ModelTrainer(tagger, corpus)
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frame_trainer.train(f'{label_type}-model', learning_rate=0.1, mini_batch_size=16, max_epochs=75, train_with_dev=False)
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if __name__ == '__main__':
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train_model("frame")
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# train_model('slot', field_parsers={'slot': nolabel2o})
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199
nlu_utils.py
199
nlu_utils.py
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from flair.data import Sentence
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from flair.datasets import FlairDatapointDataset
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def nolabel2o(line, i):
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return 'O' if line[i] == 'NoLabel' else line[i]
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def conllu2flair(sentences, label=None):
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if label == "frame":
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return conllu2flair_frame(sentences, label)
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else:
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return conllu2flair_slot(sentences, label)
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def conllu2flair_frame(sentences, label=None):
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fsentences = []
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for sentence in sentences:
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tokens = [token["form"] for token in sentence]
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fsentence = Sentence(' '.join(tokens), use_tokenizer=False)
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for i in range(len(fsentence)):
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fsentence[i:i+1].add_label(label, sentence[i][label])
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fsentences.append(fsentence)
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return FlairDatapointDataset(fsentences)
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def conllu2flair_slot(sentences, label=None):
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fsentences = []
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for sentence in sentences:
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fsentence = Sentence(' '.join(token['form'] for token in sentence), use_tokenizer=False)
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start_idx = None
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end_idx = None
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tag = None
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if label:
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for idx, (token, ftoken) in enumerate(zip(sentence, fsentence)):
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if token[label].startswith('B-'):
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start_idx = idx
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end_idx = idx
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tag = token[label][2:]
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elif token[label].startswith('I-'):
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end_idx = idx
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elif token[label] == 'O':
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if start_idx is not None:
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fsentence[start_idx:end_idx+1].add_label(label, tag)
|
||||
start_idx = None
|
||||
end_idx = None
|
||||
tag = None
|
||||
|
||||
if start_idx is not None:
|
||||
fsentence[start_idx:end_idx+1].add_label(label, tag)
|
||||
|
||||
fsentences.append(fsentence)
|
||||
return FlairDatapointDataset(fsentences)
|
||||
|
||||
def __predict(model, csentence):
|
||||
fsentence = conllu2flair([csentence])[0]
|
||||
model.predict(fsentence)
|
||||
return fsentence
|
||||
|
||||
def __csentence(sentence, label_type):
|
||||
if label_type == "frame":
|
||||
return [{'form': word } for word in sentence]
|
||||
else:
|
||||
return [{'form': word, 'slot': 'O'} for word in sentence]
|
||||
|
||||
def predict_single(model, sentence, label_type):
|
||||
csentence = __csentence(sentence, label_type)
|
||||
fsentence = __predict(model, csentence)
|
||||
intent = {}
|
||||
|
||||
for span in fsentence.get_spans(label_type):
|
||||
tag = span.get_label(label_type).value
|
||||
if tag in intent:
|
||||
intent[tag] += 1
|
||||
else:
|
||||
intent[tag] = 1
|
||||
|
||||
return max(intent, key=intent.get)
|
||||
|
||||
def predict_multiple(model, sentence, label_type):
|
||||
csentence = __csentence(sentence, label_type)
|
||||
fsentence = __predict(model, csentence)
|
||||
|
||||
return set(span.get_label(label_type).value for span in fsentence.get_spans(label_type))
|
||||
|
||||
def predict_and_annotate(model, sentence, label_type):
|
||||
csentence = __csentence(sentence, label_type)
|
||||
fsentence = __predict(model, csentence)
|
||||
|
||||
for span in fsentence.get_spans(label_type):
|
||||
tag = span.get_label(label_type).value
|
||||
if label_type == "frame":
|
||||
csentence[span.tokens[0].idx-1]['frame'] = tag
|
||||
else:
|
||||
csentence[span.tokens[0].idx - 1]['slot'] = f'B-{tag}'
|
||||
for token in span.tokens[1:]:
|
||||
csentence[token.idx - 1]['slot'] = f'I-{tag}'
|
||||
|
||||
from flair.data import Sentence
|
||||
from flair.datasets import FlairDatapointDataset
|
||||
|
||||
def nolabel2o(line, i):
|
||||
return 'O' if line[i] == 'NoLabel' else line[i]
|
||||
|
||||
def conllu2flair(sentences, label=None):
|
||||
if label == "frame":
|
||||
return conllu2flair_frame(sentences, label)
|
||||
else:
|
||||
return conllu2flair_slot(sentences, label)
|
||||
|
||||
def conllu2flair_frame(sentences, label=None):
|
||||
fsentences = []
|
||||
for sentence in sentences:
|
||||
tokens = [token["form"] for token in sentence]
|
||||
fsentence = Sentence(' '.join(tokens), use_tokenizer=False)
|
||||
|
||||
for i in range(len(fsentence)):
|
||||
fsentence[i:i+1].add_label(label, sentence[i][label])
|
||||
|
||||
fsentences.append(fsentence)
|
||||
|
||||
return FlairDatapointDataset(fsentences)
|
||||
|
||||
def conllu2flair_slot(sentences, label=None):
|
||||
fsentences = []
|
||||
for sentence in sentences:
|
||||
fsentence = Sentence(' '.join(token['form'] for token in sentence), use_tokenizer=False)
|
||||
start_idx = None
|
||||
end_idx = None
|
||||
tag = None
|
||||
|
||||
if label:
|
||||
for idx, (token, ftoken) in enumerate(zip(sentence, fsentence)):
|
||||
if token[label].startswith('B-'):
|
||||
if start_idx is not None:
|
||||
fsentence[start_idx:end_idx+1].add_label(label, tag)
|
||||
start_idx = idx
|
||||
end_idx = idx
|
||||
tag = token[label][2:]
|
||||
elif token[label].startswith('I-'):
|
||||
end_idx = idx
|
||||
elif token[label] == 'O':
|
||||
if start_idx is not None:
|
||||
fsentence[start_idx:end_idx+1].add_label(label, tag)
|
||||
start_idx = None
|
||||
end_idx = None
|
||||
tag = None
|
||||
|
||||
if start_idx is not None:
|
||||
fsentence[start_idx:end_idx+1].add_label(label, tag)
|
||||
|
||||
fsentences.append(fsentence)
|
||||
return FlairDatapointDataset(fsentences)
|
||||
|
||||
def __predict(model, csentence):
|
||||
fsentence = conllu2flair([csentence])[0]
|
||||
model.predict(fsentence)
|
||||
return fsentence
|
||||
|
||||
def __csentence(sentence, label_type):
|
||||
if label_type == "frame":
|
||||
return [{'form': word } for word in sentence]
|
||||
else:
|
||||
return [{'form': word, 'slot': 'O'} for word in sentence]
|
||||
|
||||
def predict_single(model, sentence, label_type):
|
||||
csentence = __csentence(sentence, label_type)
|
||||
fsentence = __predict(model, csentence)
|
||||
intent = {}
|
||||
|
||||
for span in fsentence.get_spans(label_type):
|
||||
tag = span.get_label(label_type).value
|
||||
if tag in intent:
|
||||
intent[tag] += 1
|
||||
else:
|
||||
intent[tag] = 1
|
||||
|
||||
return max(intent, key=intent.get)
|
||||
|
||||
def predict_multiple(model, sentence, label_type):
|
||||
csentence = __csentence(sentence, label_type)
|
||||
fsentence = __predict(model, csentence)
|
||||
|
||||
return set(span.get_label(label_type).value for span in fsentence.get_spans(label_type))
|
||||
|
||||
def predict_and_annotate(model, sentence, label_type):
|
||||
csentence = __csentence(sentence, label_type)
|
||||
fsentence = __predict(model, csentence)
|
||||
|
||||
for span in fsentence.get_spans(label_type):
|
||||
tag = span.get_label(label_type).value
|
||||
if label_type == "frame":
|
||||
csentence[span.tokens[0].idx-1]['frame'] = tag
|
||||
else:
|
||||
csentence[span.tokens[0].idx - 1]['slot'] = f'B-{tag}'
|
||||
for token in span.tokens[1:]:
|
||||
csentence[token.idx - 1]['slot'] = f'I-{tag}'
|
||||
|
||||
return csentence
|
||||
Loading…
Reference in New Issue
Block a user