62 lines
2.1 KiB
Python
62 lines
2.1 KiB
Python
|
from conllu import parse_incr
|
||
|
|
from tabulate import tabulate
|
||
|
|
from flair.data import Corpus, Sentence, Token
|
||
|
|
from flair.datasets import SentenceDataset
|
||
|
|
from flair.embeddings import StackedEmbeddings
|
||
|
|
from flair.embeddings import WordEmbeddings
|
||
|
|
from flair.embeddings import CharacterEmbeddings
|
||
|
|
from flair.embeddings import FlairEmbeddings
|
||
|
|
from flair.models import SequenceTagger
|
||
|
|
from flair.trainers import ModelTrainer
|
||
|
|
|
||
|
|
# skrypt do trenowania modelu NLU dla slotów
|
||
|
|
|
||
|
|
# determinizacja obliczeń
|
||
|
|
import random
|
||
|
|
import torch
|
||
|
|
random.seed(42)
|
||
|
|
torch.manual_seed(42)
|
||
|
|
|
||
|
|
if torch.cuda.is_available():
|
||
|
|
torch.cuda.manual_seed(0)
|
||
|
|
torch.cuda.manual_seed_all(0)
|
||
|
|
torch.backends.cudnn.enabled = False
|
||
|
|
torch.backends.cudnn.benchmark = False
|
||
|
|
torch.backends.cudnn.deterministic = True
|
||
|
|
|
||
|
|
def nolabel2o(line, i):
|
||
|
|
return 'O' if line[i] == 'NoLabel' else line[i]
|
||
|
|
|
||
|
|
def conllu2flair(sentences, label=None):
|
||
|
|
fsentences = []
|
||
|
|
for sentence in sentences:
|
||
|
|
fsentence = Sentence()
|
||
|
|
for token in sentence:
|
||
|
|
ftoken = Token(token['form'])
|
||
|
|
if label:
|
||
|
|
ftoken.add_tag(label, token[label])
|
||
|
|
fsentence.add_token(ftoken)
|
||
|
|
fsentences.append(fsentence)
|
||
|
|
return SentenceDataset(fsentences)
|
||
|
|
|
||
|
|
fields = ['id', 'form', 'frame', 'slot']
|
||
|
|
|
||
|
|
with open('data/train.conllu', encoding='utf-8') as trainfile:
|
||
|
|
slot_trainset = list(parse_incr(trainfile, fields=fields, field_parsers={'slot': nolabel2o}))
|
||
|
|
|
||
|
|
slot_corpus = Corpus(train=conllu2flair(slot_trainset, 'slot'), test=conllu2flair(slot_trainset, 'slot'))
|
||
|
|
|
||
|
|
slot_tag_dictionary = slot_corpus.make_tag_dictionary(tag_type='slot')
|
||
|
|
|
||
|
|
embedding_types = [
|
||
|
|
WordEmbeddings('pl'),
|
||
|
|
FlairEmbeddings('pl-forward'),
|
||
|
|
FlairEmbeddings('pl-backward'),
|
||
|
|
CharacterEmbeddings(),
|
||
|
|
]
|
||
|
|
|
||
|
|
embeddings = StackedEmbeddings(embeddings=embedding_types)
|
||
|
|
slot_tagger = SequenceTagger(hidden_size=256, embeddings=embeddings, tag_dictionary=slot_tag_dictionary, tag_type='slot', use_crf=True)
|
||
|
|
|
||
|
|
slot_trainer = ModelTrainer(slot_tagger, slot_corpus)
|
||
|
|
slot_trainer.train('slot-model', learning_rate=0.1, mini_batch_size=24, max_epochs=100, train_with_dev=False)
|