redid NLU task files upload
This commit is contained in:
Kacper Dudzic
parent
c25548d9ea
commit
4de73a4a11
@ -4,6 +4,10 @@ import pandas as pd
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from nltk.tokenize import word_tokenize
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import re
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import random
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import nltk
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#nltk.download('punkt')
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class LineContent:
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@ -58,9 +62,11 @@ def process_file(file):
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if email:
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email_address = email.group()
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text = text.replace(email_address, '@')
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text = text.replace("'", "")
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tokens = word_tokenize(text)
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tokens = [token.replace('@', email_address) for token in tokens]
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else:
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text = text.replace("'", "")
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tokens = word_tokenize(text)
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return tokens
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text_tokens = tokenize(text)
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@ -90,7 +96,7 @@ def process_file(file):
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lines_contents = []
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for _, row in df.iterrows():
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if row[0] == 'user' and row[1]:
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# if row[1]:
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#if row[1]:
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text = row[1]
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intents = get_intents(row[2])
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slots = get_slots(row[2])
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@ -1,85 +1,85 @@
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from conllu import parse_incr
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from flair.data import Corpus, Sentence, Token
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from flair.datasets import SentenceDataset
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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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import random
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import torch
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from tabulate import tabulate
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fields = ['id', 'form', 'frame', 'slot']
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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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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(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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model.predict(fsentence)
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return [(token, ftoken.get_tag('slot').value) for token, ftoken in zip(sentence, fsentence)]
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with open('train-pl-full.conllu', encoding='utf-8') as trainfile:
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trainset = list(parse_incr(trainfile, fields=fields, field_parsers={'slot': nolabel2o}))
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with open('test-pl-full.conllu', encoding='utf-8') as testfile:
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testset = list(parse_incr(testfile, fields=fields, field_parsers={'slot': nolabel2o}))
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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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corpus = Corpus(train=conllu2flair(trainset, 'slot'), test=conllu2flair(testset, 'slot'))
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tag_dictionary = corpus.make_tag_dictionary(tag_type='slot')
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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,
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tag_dictionary=tag_dictionary,
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tag_type='slot', use_crf=True)
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"""
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trainer = ModelTrainer(tagger, corpus)
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trainer.train('slot-model-pl',
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learning_rate=0.1,
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mini_batch_size=32,
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max_epochs=10,
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train_with_dev=True)
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"""
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try:
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model = SequenceTagger.load('slot-model-pl/best-model.pt')
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except:
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model = SequenceTagger.load('slot-model-pl/final-model.pt')
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print(tabulate(predict(model, 'Jeden bilet na imię Jan Kowalski na film Batman'.split())))
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from conllu import parse_incr
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from flair.data import Corpus, Sentence, Token
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from flair.datasets import SentenceDataset
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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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import random
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import torch
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from tabulate import tabulate
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fields = ['id', 'form', 'frame', 'slot']
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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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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(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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model.predict(fsentence)
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return [(token, ftoken.get_tag('slot').value) for token, ftoken in zip(sentence, fsentence)]
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with open('train-pl.conllu', encoding='utf-8') as trainfile:
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trainset = list(parse_incr(trainfile, fields=fields, field_parsers={'slot': nolabel2o}))
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with open('test-pl.conllu', encoding='utf-8') as testfile:
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testset = list(parse_incr(testfile, fields=fields, field_parsers={'slot': nolabel2o}))
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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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corpus = Corpus(train=conllu2flair(trainset, 'slot'), test=conllu2flair(testset, 'slot'))
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tag_dictionary = corpus.make_tag_dictionary(tag_type='slot')
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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,
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tag_dictionary=tag_dictionary,
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tag_type='slot', use_crf=True)
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trainer = ModelTrainer(tagger, corpus)
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trainer.train('slot-model-pl',
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learning_rate=0.1,
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mini_batch_size=32,
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max_epochs=10,
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train_with_dev=True)
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try:
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model = SequenceTagger.load('slot-model-pl/best-model.pt')
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except:
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model = SequenceTagger.load('slot-model-pl/final-model.pt')
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print(tabulate(predict(model, 'Jeden bilet na imię Jan Kowalski na film Batman'.split())))
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@ -1,166 +0,0 @@
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poproszę O O
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listę B-goal O
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filmów I-goal O
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granych O O
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jutro B-date B-date
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wieczorem B-interval I-date
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wagon O O
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z O O
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przedziałami O O
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miejsce O O
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przy O O
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oknie O O
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23032022 O O
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o B-goal B-goal
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której I-goal I-goal
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jest O O
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na B-title B-title
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noże I-title I-title
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a O O
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jakie O B-goal
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są O I-goal
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proszę O O
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o O O
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godzine O B-goal
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20:19 B-time I-goal
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a O O
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jakie O B-goal
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są O I-goal
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dostępne O I-goal
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ok O O
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o O B-goal
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jakich O I-goal
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godzinach O I-goal
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grają O O
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te O O
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filmy O O
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wszystkie O O
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dziękuję O O
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witam O O
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jakie B-goal B-goal
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filmy I-goal I-goal
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są I-goal I-goal
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teraz O I-goal
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w O O
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kinach O O
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wybieram O O
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godzine O B-date
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12:00 B-time I-date
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29032022 O O
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halo O O
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halo O O
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123123 O O
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podaj O O
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więcej O O
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informacji O O
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o O O
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seansach O O
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ok O O
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z B-area B-area
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tyłu I-area I-area
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sali I-area O
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nie I-area O
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na I-area B-area
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samym I-area I-area
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końcu I-area I-area
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na B-area B-area
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środku I-area I-area
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do O O
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widzenia O O
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a O O
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z B-area B-area
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przodu I-area I-area
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gdzieś O O
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dzień O O
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dobry O O
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jeden B-quantity B-quantity
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normalny I-quantity O
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i I-quantity O
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ulgowy I-quantity O
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witam O O
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dzień O O
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dobry O O
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czy O O
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sš O O
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wcześniejsze B-goal B-goal
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seanse I-goal I-goal
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kim O O
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jest O O
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senior O O
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3 B-quantity B-quantity
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dzień O O
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dobry O O
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co B-goal O
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można I-goal O
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obejrzeć I-goal O
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w O B-date
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kwietniu B-interval I-date
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tak O O
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no O O
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to O O
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jakoś O O
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niech O O
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będzie O O
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jakoś O O
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to B-title O
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będzie I-title O
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wybieram O O
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na B-title B-title
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noże I-title I-title
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o B-time B-time
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09:30 I-time B-time
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idę O O
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na O O
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drugą B-time B-time
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na O O
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batmana B-title B-title
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trzy B-quantity B-quantity
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bileciki O O
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na B-time B-time
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19:00 I-time B-time
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na O O
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batmana B-title B-title
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na B-area B-area
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środku I-area I-area
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co O O
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leci O O
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w B-date B-date
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poniedziałek I-date I-date
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@ -1,11 +0,0 @@
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EPOCH TIMESTAMP BAD_EPOCHS LEARNING_RATE TRAIN_LOSS
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1 15:45:49 0 0.1000 1.107883760221383
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2 15:46:06 0 0.1000 0.724391172370514
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3 15:46:23 0 0.1000 0.6198507147675428
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4 15:46:42 0 0.1000 0.5637349612763847
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5 15:46:58 0 0.1000 0.48588470330256117
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6 15:47:14 0 0.1000 0.4225153886549188
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7 15:47:30 0 0.1000 0.38841035381494515
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8 15:47:45 0 0.1000 0.3469537376117912
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9 15:48:00 0 0.1000 0.30912264277005586
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10 15:48:16 1 0.1000 0.31141101694209966
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File diff suppressed because it is too large
Load Diff
@ -1,261 +0,0 @@
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2022-05-02 15:44:09,185 ----------------------------------------------------------------------------------------------------
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2022-05-02 15:44:09,185 Model: "SequenceTagger(
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(embeddings): StackedEmbeddings(
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(list_embedding_0): WordEmbeddings('pl')
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(list_embedding_1): FlairEmbeddings(
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(lm): LanguageModel(
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(drop): Dropout(p=0.25, inplace=False)
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(encoder): Embedding(1602, 100)
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(rnn): LSTM(100, 2048)
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(decoder): Linear(in_features=2048, out_features=1602, bias=True)
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)
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)
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(list_embedding_2): FlairEmbeddings(
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(lm): LanguageModel(
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(drop): Dropout(p=0.25, inplace=False)
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(encoder): Embedding(1602, 100)
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(rnn): LSTM(100, 2048)
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(decoder): Linear(in_features=2048, out_features=1602, bias=True)
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)
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)
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(list_embedding_3): CharacterEmbeddings(
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(char_embedding): Embedding(275, 25)
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(char_rnn): LSTM(25, 25, bidirectional=True)
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)
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)
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(word_dropout): WordDropout(p=0.05)
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(locked_dropout): LockedDropout(p=0.5)
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(embedding2nn): Linear(in_features=4446, out_features=4446, bias=True)
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(rnn): LSTM(4446, 256, batch_first=True, bidirectional=True)
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(linear): Linear(in_features=512, out_features=50, bias=True)
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(beta): 1.0
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(weights): None
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(weight_tensor) None
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)"
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2022-05-02 15:44:09,185 ----------------------------------------------------------------------------------------------------
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2022-05-02 15:44:09,185 Corpus: "Corpus: 735 train + 82 dev + 152 test sentences"
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2022-05-02 15:44:09,185 ----------------------------------------------------------------------------------------------------
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2022-05-02 15:44:09,185 Parameters:
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2022-05-02 15:44:09,185 - learning_rate: "0.1"
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2022-05-02 15:44:09,185 - mini_batch_size: "32"
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2022-05-02 15:44:09,185 - patience: "3"
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2022-05-02 15:44:09,185 - anneal_factor: "0.5"
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2022-05-02 15:44:09,185 - max_epochs: "10"
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2022-05-02 15:44:09,185 - shuffle: "True"
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2022-05-02 15:44:09,185 - train_with_dev: "True"
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2022-05-02 15:44:09,185 - batch_growth_annealing: "False"
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2022-05-02 15:44:09,185 ----------------------------------------------------------------------------------------------------
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2022-05-02 15:44:09,185 Model training base path: "slot-model-pl"
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2022-05-02 15:44:09,185 ----------------------------------------------------------------------------------------------------
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2022-05-02 15:44:09,185 Device: cpu
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2022-05-02 15:44:09,185 ----------------------------------------------------------------------------------------------------
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2022-05-02 15:44:09,185 Embeddings storage mode: cpu
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2022-05-02 15:44:09,212 ----------------------------------------------------------------------------------------------------
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2022-05-02 15:44:12,896 epoch 1 - iter 2/26 - loss 5.40706334 - samples/sec: 17.37 - lr: 0.100000
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2022-05-02 15:44:17,195 epoch 1 - iter 4/26 - loss 4.38706093 - samples/sec: 14.89 - lr: 0.100000
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2022-05-02 15:44:20,984 epoch 1 - iter 6/26 - loss 3.63759864 - samples/sec: 16.90 - lr: 0.100000
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2022-05-02 15:44:25,378 epoch 1 - iter 8/26 - loss 3.26681995 - samples/sec: 14.57 - lr: 0.100000
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2022-05-02 15:44:29,757 epoch 1 - iter 10/26 - loss 3.05881263 - samples/sec: 14.62 - lr: 0.100000
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2022-05-02 15:44:40,091 epoch 1 - iter 12/26 - loss 2.53006141 - samples/sec: 6.19 - lr: 0.100000
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2022-05-02 15:44:52,707 epoch 1 - iter 14/26 - loss 1.93704781 - samples/sec: 5.07 - lr: 0.100000
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2022-05-02 15:45:02,080 epoch 1 - iter 16/26 - loss 1.63138431 - samples/sec: 6.83 - lr: 0.100000
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2022-05-02 15:45:14,009 epoch 1 - iter 18/26 - loss 1.40000228 - samples/sec: 5.37 - lr: 0.100000
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2022-05-02 15:45:23,287 epoch 1 - iter 20/26 - loss 1.23378287 - samples/sec: 6.90 - lr: 0.100000
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2022-05-02 15:45:34,691 epoch 1 - iter 22/26 - loss 1.12719827 - samples/sec: 5.61 - lr: 0.100000
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2022-05-02 15:45:40,330 epoch 1 - iter 24/26 - loss 1.13188836 - samples/sec: 11.35 - lr: 0.100000
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2022-05-02 15:45:49,236 epoch 1 - iter 26/26 - loss 1.10788376 - samples/sec: 7.19 - lr: 0.100000
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2022-05-02 15:45:49,236 ----------------------------------------------------------------------------------------------------
|
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2022-05-02 15:45:49,236 EPOCH 1 done: loss 1.1079 - lr 0.1000000
|
||||
2022-05-02 15:45:49,236 BAD EPOCHS (no improvement): 0
|
||||
2022-05-02 15:45:49,237 ----------------------------------------------------------------------------------------------------
|
||||
2022-05-02 15:45:50,285 epoch 2 - iter 2/26 - loss 1.25997738 - samples/sec: 61.30 - lr: 0.100000
|
||||
2022-05-02 15:45:51,915 epoch 2 - iter 4/26 - loss 0.91321364 - samples/sec: 39.27 - lr: 0.100000
|
||||
2022-05-02 15:45:53,048 epoch 2 - iter 6/26 - loss 0.97971206 - samples/sec: 56.50 - lr: 0.100000
|
||||
2022-05-02 15:45:54,340 epoch 2 - iter 8/26 - loss 0.87838664 - samples/sec: 49.56 - lr: 0.100000
|
||||
2022-05-02 15:45:55,485 epoch 2 - iter 10/26 - loss 0.86177694 - samples/sec: 55.90 - lr: 0.100000
|
||||
2022-05-02 15:45:56,488 epoch 2 - iter 12/26 - loss 0.81463133 - samples/sec: 63.85 - lr: 0.100000
|
||||
2022-05-02 15:45:57,918 epoch 2 - iter 14/26 - loss 0.76334644 - samples/sec: 44.79 - lr: 0.100000
|
||||
2022-05-02 15:45:59,393 epoch 2 - iter 16/26 - loss 0.78542696 - samples/sec: 43.41 - lr: 0.100000
|
||||
2022-05-02 15:46:00,673 epoch 2 - iter 18/26 - loss 0.74084630 - samples/sec: 49.99 - lr: 0.100000
|
||||
2022-05-02 15:46:02,245 epoch 2 - iter 20/26 - loss 0.71586100 - samples/sec: 40.74 - lr: 0.100000
|
||||
2022-05-02 15:46:04,051 epoch 2 - iter 22/26 - loss 0.71469797 - samples/sec: 35.45 - lr: 0.100000
|
||||
2022-05-02 15:46:05,132 epoch 2 - iter 24/26 - loss 0.71315625 - samples/sec: 59.21 - lr: 0.100000
|
||||
2022-05-02 15:46:06,356 epoch 2 - iter 26/26 - loss 0.72439117 - samples/sec: 52.36 - lr: 0.100000
|
||||
2022-05-02 15:46:06,356 ----------------------------------------------------------------------------------------------------
|
||||
2022-05-02 15:46:06,356 EPOCH 2 done: loss 0.7244 - lr 0.1000000
|
||||
2022-05-02 15:46:06,356 BAD EPOCHS (no improvement): 0
|
||||
2022-05-02 15:46:06,356 ----------------------------------------------------------------------------------------------------
|
||||
2022-05-02 15:46:07,363 epoch 3 - iter 2/26 - loss 0.93262965 - samples/sec: 63.62 - lr: 0.100000
|
||||
2022-05-02 15:46:08,805 epoch 3 - iter 4/26 - loss 0.66342690 - samples/sec: 44.41 - lr: 0.100000
|
||||
2022-05-02 15:46:10,199 epoch 3 - iter 6/26 - loss 0.69693404 - samples/sec: 45.93 - lr: 0.100000
|
||||
2022-05-02 15:46:11,106 epoch 3 - iter 8/26 - loss 0.71254800 - samples/sec: 70.54 - lr: 0.100000
|
||||
2022-05-02 15:46:12,661 epoch 3 - iter 10/26 - loss 0.68056002 - samples/sec: 41.17 - lr: 0.100000
|
||||
2022-05-02 15:46:14,195 epoch 3 - iter 12/26 - loss 0.62003628 - samples/sec: 41.75 - lr: 0.100000
|
||||
2022-05-02 15:46:15,549 epoch 3 - iter 14/26 - loss 0.62764929 - samples/sec: 47.29 - lr: 0.100000
|
||||
2022-05-02 15:46:16,685 epoch 3 - iter 16/26 - loss 0.64616873 - samples/sec: 56.36 - lr: 0.100000
|
||||
2022-05-02 15:46:18,469 epoch 3 - iter 18/26 - loss 0.65065601 - samples/sec: 35.88 - lr: 0.100000
|
||||
2022-05-02 15:46:19,908 epoch 3 - iter 20/26 - loss 0.64878090 - samples/sec: 44.50 - lr: 0.100000
|
||||
2022-05-02 15:46:21,278 epoch 3 - iter 22/26 - loss 0.63696184 - samples/sec: 46.72 - lr: 0.100000
|
||||
2022-05-02 15:46:22,587 epoch 3 - iter 24/26 - loss 0.63006250 - samples/sec: 48.92 - lr: 0.100000
|
||||
2022-05-02 15:46:23,866 epoch 3 - iter 26/26 - loss 0.61985071 - samples/sec: 50.08 - lr: 0.100000
|
||||
2022-05-02 15:46:23,866 ----------------------------------------------------------------------------------------------------
|
||||
2022-05-02 15:46:23,866 EPOCH 3 done: loss 0.6199 - lr 0.1000000
|
||||
2022-05-02 15:46:23,866 BAD EPOCHS (no improvement): 0
|
||||
2022-05-02 15:46:23,867 ----------------------------------------------------------------------------------------------------
|
||||
2022-05-02 15:46:25,024 epoch 4 - iter 2/26 - loss 0.82507049 - samples/sec: 55.30 - lr: 0.100000
|
||||
2022-05-02 15:46:26,129 epoch 4 - iter 4/26 - loss 0.78983147 - samples/sec: 57.98 - lr: 0.100000
|
||||
2022-05-02 15:46:27,401 epoch 4 - iter 6/26 - loss 0.69410684 - samples/sec: 50.34 - lr: 0.100000
|
||||
2022-05-02 15:46:28,974 epoch 4 - iter 8/26 - loss 0.62705834 - samples/sec: 40.69 - lr: 0.100000
|
||||
2022-05-02 15:46:30,301 epoch 4 - iter 10/26 - loss 0.57534194 - samples/sec: 48.26 - lr: 0.100000
|
||||
2022-05-02 15:46:32,177 epoch 4 - iter 12/26 - loss 0.55566517 - samples/sec: 34.13 - lr: 0.100000
|
||||
2022-05-02 15:46:33,477 epoch 4 - iter 14/26 - loss 0.56243747 - samples/sec: 49.26 - lr: 0.100000
|
||||
2022-05-02 15:46:35,204 epoch 4 - iter 16/26 - loss 0.56436807 - samples/sec: 37.07 - lr: 0.100000
|
||||
2022-05-02 15:46:36,732 epoch 4 - iter 18/26 - loss 0.58195288 - samples/sec: 41.88 - lr: 0.100000
|
||||
2022-05-02 15:46:38,109 epoch 4 - iter 20/26 - loss 0.58868604 - samples/sec: 46.53 - lr: 0.100000
|
||||
2022-05-02 15:46:39,677 epoch 4 - iter 22/26 - loss 0.56758502 - samples/sec: 40.87 - lr: 0.100000
|
||||
2022-05-02 15:46:41,433 epoch 4 - iter 24/26 - loss 0.55202777 - samples/sec: 36.45 - lr: 0.100000
|
||||
2022-05-02 15:46:42,227 epoch 4 - iter 26/26 - loss 0.56373496 - samples/sec: 80.65 - lr: 0.100000
|
||||
2022-05-02 15:46:42,227 ----------------------------------------------------------------------------------------------------
|
||||
2022-05-02 15:46:42,227 EPOCH 4 done: loss 0.5637 - lr 0.1000000
|
||||
2022-05-02 15:46:42,227 BAD EPOCHS (no improvement): 0
|
||||
2022-05-02 15:46:42,228 ----------------------------------------------------------------------------------------------------
|
||||
2022-05-02 15:46:43,715 epoch 5 - iter 2/26 - loss 0.45379848 - samples/sec: 43.04 - lr: 0.100000
|
||||
2022-05-02 15:46:44,818 epoch 5 - iter 4/26 - loss 0.54424541 - samples/sec: 58.04 - lr: 0.100000
|
||||
2022-05-02 15:46:46,795 epoch 5 - iter 6/26 - loss 0.55437849 - samples/sec: 32.38 - lr: 0.100000
|
||||
2022-05-02 15:46:47,855 epoch 5 - iter 8/26 - loss 0.58448347 - samples/sec: 60.42 - lr: 0.100000
|
||||
2022-05-02 15:46:49,017 epoch 5 - iter 10/26 - loss 0.57394500 - samples/sec: 55.10 - lr: 0.100000
|
||||
2022-05-02 15:46:50,144 epoch 5 - iter 12/26 - loss 0.56309941 - samples/sec: 56.82 - lr: 0.100000
|
||||
2022-05-02 15:46:51,022 epoch 5 - iter 14/26 - loss 0.56087045 - samples/sec: 72.92 - lr: 0.100000
|
||||
2022-05-02 15:46:52,247 epoch 5 - iter 16/26 - loss 0.54126941 - samples/sec: 52.27 - lr: 0.100000
|
||||
2022-05-02 15:46:53,517 epoch 5 - iter 18/26 - loss 0.54781672 - samples/sec: 50.41 - lr: 0.100000
|
||||
2022-05-02 15:46:54,987 epoch 5 - iter 20/26 - loss 0.52409069 - samples/sec: 43.55 - lr: 0.100000
|
||||
2022-05-02 15:46:56,416 epoch 5 - iter 22/26 - loss 0.51082819 - samples/sec: 44.84 - lr: 0.100000
|
||||
2022-05-02 15:46:58,077 epoch 5 - iter 24/26 - loss 0.50232400 - samples/sec: 38.55 - lr: 0.100000
|
||||
2022-05-02 15:46:58,995 epoch 5 - iter 26/26 - loss 0.48588470 - samples/sec: 69.78 - lr: 0.100000
|
||||
2022-05-02 15:46:58,995 ----------------------------------------------------------------------------------------------------
|
||||
2022-05-02 15:46:58,995 EPOCH 5 done: loss 0.4859 - lr 0.1000000
|
||||
2022-05-02 15:46:58,995 BAD EPOCHS (no improvement): 0
|
||||
2022-05-02 15:46:58,995 ----------------------------------------------------------------------------------------------------
|
||||
2022-05-02 15:47:00,509 epoch 6 - iter 2/26 - loss 0.52375350 - samples/sec: 42.28 - lr: 0.100000
|
||||
2022-05-02 15:47:02,103 epoch 6 - iter 4/26 - loss 0.41911038 - samples/sec: 40.18 - lr: 0.100000
|
||||
2022-05-02 15:47:03,126 epoch 6 - iter 6/26 - loss 0.41424604 - samples/sec: 62.57 - lr: 0.100000
|
||||
2022-05-02 15:47:04,316 epoch 6 - iter 8/26 - loss 0.39943972 - samples/sec: 53.82 - lr: 0.100000
|
||||
2022-05-02 15:47:05,798 epoch 6 - iter 10/26 - loss 0.36462904 - samples/sec: 43.20 - lr: 0.100000
|
||||
2022-05-02 15:47:06,774 epoch 6 - iter 12/26 - loss 0.37187295 - samples/sec: 65.60 - lr: 0.100000
|
||||
2022-05-02 15:47:07,781 epoch 6 - iter 14/26 - loss 0.40622993 - samples/sec: 63.60 - lr: 0.100000
|
||||
2022-05-02 15:47:08,846 epoch 6 - iter 16/26 - loss 0.42953310 - samples/sec: 60.13 - lr: 0.100000
|
||||
2022-05-02 15:47:10,187 epoch 6 - iter 18/26 - loss 0.41096443 - samples/sec: 47.72 - lr: 0.100000
|
||||
2022-05-02 15:47:11,212 epoch 6 - iter 20/26 - loss 0.42107760 - samples/sec: 62.50 - lr: 0.100000
|
||||
2022-05-02 15:47:12,138 epoch 6 - iter 22/26 - loss 0.42309019 - samples/sec: 69.15 - lr: 0.100000
|
||||
2022-05-02 15:47:13,311 epoch 6 - iter 24/26 - loss 0.42768651 - samples/sec: 54.57 - lr: 0.100000
|
||||
2022-05-02 15:47:14,615 epoch 6 - iter 26/26 - loss 0.42251539 - samples/sec: 49.12 - lr: 0.100000
|
||||
2022-05-02 15:47:14,615 ----------------------------------------------------------------------------------------------------
|
||||
2022-05-02 15:47:14,615 EPOCH 6 done: loss 0.4225 - lr 0.1000000
|
||||
2022-05-02 15:47:14,615 BAD EPOCHS (no improvement): 0
|
||||
2022-05-02 15:47:14,615 ----------------------------------------------------------------------------------------------------
|
||||
2022-05-02 15:47:15,953 epoch 7 - iter 2/26 - loss 0.42888915 - samples/sec: 47.86 - lr: 0.100000
|
||||
2022-05-02 15:47:16,988 epoch 7 - iter 4/26 - loss 0.46386105 - samples/sec: 61.89 - lr: 0.100000
|
||||
2022-05-02 15:47:17,972 epoch 7 - iter 6/26 - loss 0.45750826 - samples/sec: 65.04 - lr: 0.100000
|
||||
2022-05-02 15:47:19,035 epoch 7 - iter 8/26 - loss 0.45111557 - samples/sec: 60.26 - lr: 0.100000
|
||||
2022-05-02 15:47:20,138 epoch 7 - iter 10/26 - loss 0.44598492 - samples/sec: 58.08 - lr: 0.100000
|
||||
2022-05-02 15:47:21,221 epoch 7 - iter 12/26 - loss 0.43062620 - samples/sec: 59.11 - lr: 0.100000
|
||||
2022-05-02 15:47:22,486 epoch 7 - iter 14/26 - loss 0.43319146 - samples/sec: 50.61 - lr: 0.100000
|
||||
2022-05-02 15:47:23,844 epoch 7 - iter 16/26 - loss 0.40657923 - samples/sec: 47.16 - lr: 0.100000
|
||||
2022-05-02 15:47:25,007 epoch 7 - iter 18/26 - loss 0.41484192 - samples/sec: 55.05 - lr: 0.100000
|
||||
2022-05-02 15:47:26,325 epoch 7 - iter 20/26 - loss 0.41555710 - samples/sec: 48.58 - lr: 0.100000
|
||||
2022-05-02 15:47:27,600 epoch 7 - iter 22/26 - loss 0.40336973 - samples/sec: 50.21 - lr: 0.100000
|
||||
2022-05-02 15:47:29,044 epoch 7 - iter 24/26 - loss 0.39532046 - samples/sec: 44.33 - lr: 0.100000
|
||||
2022-05-02 15:47:30,078 epoch 7 - iter 26/26 - loss 0.38841035 - samples/sec: 61.93 - lr: 0.100000
|
||||
2022-05-02 15:47:30,079 ----------------------------------------------------------------------------------------------------
|
||||
2022-05-02 15:47:30,079 EPOCH 7 done: loss 0.3884 - lr 0.1000000
|
||||
2022-05-02 15:47:30,079 BAD EPOCHS (no improvement): 0
|
||||
2022-05-02 15:47:30,079 ----------------------------------------------------------------------------------------------------
|
||||
2022-05-02 15:47:31,357 epoch 8 - iter 2/26 - loss 0.41543718 - samples/sec: 50.11 - lr: 0.100000
|
||||
2022-05-02 15:47:32,538 epoch 8 - iter 4/26 - loss 0.32899498 - samples/sec: 54.19 - lr: 0.100000
|
||||
2022-05-02 15:47:33,686 epoch 8 - iter 6/26 - loss 0.35113539 - samples/sec: 55.79 - lr: 0.100000
|
||||
2022-05-02 15:47:34,725 epoch 8 - iter 8/26 - loss 0.38507402 - samples/sec: 61.58 - lr: 0.100000
|
||||
2022-05-02 15:47:35,995 epoch 8 - iter 10/26 - loss 0.42831411 - samples/sec: 50.42 - lr: 0.100000
|
||||
2022-05-02 15:47:37,049 epoch 8 - iter 12/26 - loss 0.39097058 - samples/sec: 60.79 - lr: 0.100000
|
||||
2022-05-02 15:47:38,008 epoch 8 - iter 14/26 - loss 0.37596686 - samples/sec: 66.72 - lr: 0.100000
|
||||
2022-05-02 15:47:39,462 epoch 8 - iter 16/26 - loss 0.37649604 - samples/sec: 44.05 - lr: 0.100000
|
||||
2022-05-02 15:47:40,655 epoch 8 - iter 18/26 - loss 0.37892339 - samples/sec: 53.64 - lr: 0.100000
|
||||
2022-05-02 15:47:42,031 epoch 8 - iter 20/26 - loss 0.35924042 - samples/sec: 46.54 - lr: 0.100000
|
||||
2022-05-02 15:47:43,123 epoch 8 - iter 22/26 - loss 0.35480360 - samples/sec: 58.65 - lr: 0.100000
|
||||
2022-05-02 15:47:44,286 epoch 8 - iter 24/26 - loss 0.34975662 - samples/sec: 55.03 - lr: 0.100000
|
||||
2022-05-02 15:47:45,065 epoch 8 - iter 26/26 - loss 0.34695374 - samples/sec: 82.23 - lr: 0.100000
|
||||
2022-05-02 15:47:45,065 ----------------------------------------------------------------------------------------------------
|
||||
2022-05-02 15:47:45,065 EPOCH 8 done: loss 0.3470 - lr 0.1000000
|
||||
2022-05-02 15:47:45,065 BAD EPOCHS (no improvement): 0
|
||||
2022-05-02 15:47:45,066 ----------------------------------------------------------------------------------------------------
|
||||
2022-05-02 15:47:46,190 epoch 9 - iter 2/26 - loss 0.25508414 - samples/sec: 56.93 - lr: 0.100000
|
||||
2022-05-02 15:47:47,453 epoch 9 - iter 4/26 - loss 0.32180418 - samples/sec: 50.72 - lr: 0.100000
|
||||
2022-05-02 15:47:48,461 epoch 9 - iter 6/26 - loss 0.40408790 - samples/sec: 63.48 - lr: 0.100000
|
||||
2022-05-02 15:47:49,701 epoch 9 - iter 8/26 - loss 0.39779257 - samples/sec: 51.64 - lr: 0.100000
|
||||
2022-05-02 15:47:51,048 epoch 9 - iter 10/26 - loss 0.36724150 - samples/sec: 47.52 - lr: 0.100000
|
||||
2022-05-02 15:47:51,922 epoch 9 - iter 12/26 - loss 0.35932055 - samples/sec: 73.33 - lr: 0.100000
|
||||
2022-05-02 15:47:53,117 epoch 9 - iter 14/26 - loss 0.34947437 - samples/sec: 53.57 - lr: 0.100000
|
||||
2022-05-02 15:47:54,265 epoch 9 - iter 16/26 - loss 0.32652718 - samples/sec: 55.77 - lr: 0.100000
|
||||
2022-05-02 15:47:55,487 epoch 9 - iter 18/26 - loss 0.32168879 - samples/sec: 52.41 - lr: 0.100000
|
||||
2022-05-02 15:47:56,483 epoch 9 - iter 20/26 - loss 0.32835642 - samples/sec: 64.28 - lr: 0.100000
|
||||
2022-05-02 15:47:57,790 epoch 9 - iter 22/26 - loss 0.33238740 - samples/sec: 48.98 - lr: 0.100000
|
||||
2022-05-02 15:47:59,047 epoch 9 - iter 24/26 - loss 0.32465148 - samples/sec: 50.93 - lr: 0.100000
|
||||
2022-05-02 15:48:00,176 epoch 9 - iter 26/26 - loss 0.30912264 - samples/sec: 56.73 - lr: 0.100000
|
||||
2022-05-02 15:48:00,176 ----------------------------------------------------------------------------------------------------
|
||||
2022-05-02 15:48:00,176 EPOCH 9 done: loss 0.3091 - lr 0.1000000
|
||||
2022-05-02 15:48:00,176 BAD EPOCHS (no improvement): 0
|
||||
2022-05-02 15:48:00,176 ----------------------------------------------------------------------------------------------------
|
||||
2022-05-02 15:48:01,533 epoch 10 - iter 2/26 - loss 0.34254425 - samples/sec: 47.18 - lr: 0.100000
|
||||
2022-05-02 15:48:02,801 epoch 10 - iter 4/26 - loss 0.37900189 - samples/sec: 50.52 - lr: 0.100000
|
||||
2022-05-02 15:48:03,912 epoch 10 - iter 6/26 - loss 0.33156605 - samples/sec: 57.61 - lr: 0.100000
|
||||
2022-05-02 15:48:05,257 epoch 10 - iter 8/26 - loss 0.30826664 - samples/sec: 47.58 - lr: 0.100000
|
||||
2022-05-02 15:48:06,496 epoch 10 - iter 10/26 - loss 0.32724932 - samples/sec: 51.71 - lr: 0.100000
|
||||
2022-05-02 15:48:07,790 epoch 10 - iter 12/26 - loss 0.30998078 - samples/sec: 49.46 - lr: 0.100000
|
||||
2022-05-02 15:48:09,009 epoch 10 - iter 14/26 - loss 0.30504032 - samples/sec: 52.52 - lr: 0.100000
|
||||
2022-05-02 15:48:10,539 epoch 10 - iter 16/26 - loss 0.28721872 - samples/sec: 41.87 - lr: 0.100000
|
||||
2022-05-02 15:48:11,646 epoch 10 - iter 18/26 - loss 0.29072309 - samples/sec: 57.84 - lr: 0.100000
|
||||
2022-05-02 15:48:12,706 epoch 10 - iter 20/26 - loss 0.30101217 - samples/sec: 60.40 - lr: 0.100000
|
||||
2022-05-02 15:48:13,994 epoch 10 - iter 22/26 - loss 0.30494834 - samples/sec: 49.71 - lr: 0.100000
|
||||
2022-05-02 15:48:15,298 epoch 10 - iter 24/26 - loss 0.31061478 - samples/sec: 49.09 - lr: 0.100000
|
||||
2022-05-02 15:48:16,150 epoch 10 - iter 26/26 - loss 0.31141102 - samples/sec: 75.23 - lr: 0.100000
|
||||
2022-05-02 15:48:16,150 ----------------------------------------------------------------------------------------------------
|
||||
2022-05-02 15:48:16,150 EPOCH 10 done: loss 0.3114 - lr 0.1000000
|
||||
2022-05-02 15:48:16,150 BAD EPOCHS (no improvement): 1
|
||||
2022-05-02 15:48:46,752 ----------------------------------------------------------------------------------------------------
|
||||
2022-05-02 15:48:46,768 Testing using last state of model ...
|
||||
2022-05-02 15:49:06,966 0.3 0.1846 0.2286 0.1364
|
||||
2022-05-02 15:49:06,967
|
||||
Results:
|
||||
- F-score (micro) 0.2286
|
||||
- F-score (macro) 0.1296
|
||||
- Accuracy 0.1364
|
||||
|
||||
By class:
|
||||
precision recall f1-score support
|
||||
|
||||
quantity 0.3571 0.8333 0.5000 6
|
||||
title 0.2857 0.2000 0.2353 10
|
||||
goal 0.0000 0.0000 0.0000 10
|
||||
time 0.4000 0.2222 0.2857 9
|
||||
date 0.6667 0.5000 0.5714 4
|
||||
area 0.0000 0.0000 0.0000 3
|
||||
interval 0.0000 0.0000 0.0000 1
|
||||
movie 0.0000 0.0000 0.0000 3
|
||||
phone 0.0000 0.0000 0.0000 3
|
||||
seat 0.0000 0.0000 0.0000 2
|
||||
hour 0.0000 0.0000 0.0000 2
|
||||
row 0.0000 0.0000 0.0000 2
|
||||
ticketnumber 0.0000 0.0000 0.0000 2
|
||||
name 1.0000 1.0000 1.0000 1
|
||||
e-mail 0.0000 0.0000 0.0000 2
|
||||
ticketsnumber 0.0000 0.0000 0.0000 1
|
||||
sit_place 0.0000 0.0000 0.0000 1
|
||||
email 0.0000 0.0000 0.0000 1
|
||||
bankAccountNumber 0.0000 0.0000 0.0000 1
|
||||
issue 0.0000 0.0000 0.0000 1
|
||||
|
||||
micro avg 0.3000 0.1846 0.2286 65
|
||||
macro avg 0.1355 0.1378 0.1296 65
|
||||
weighted avg 0.1887 0.1846 0.1725 65
|
||||
samples avg 0.1364 0.1364 0.1364 65
|
||||
|
||||
2022-05-02 15:49:06,967 ----------------------------------------------------------------------------------------------------
|
||||
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Load Diff
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Load Diff
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Load Diff
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Load Diff
Loading…
Reference in New Issue
Block a user