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自作データで学習したモデルを再度読み込んで2回目の学習を実行[Flair]

自作データを使って2回以上の学習を回したときにハマったのでまとめる.

筆者が自作データでNERの学習を行なったときに,1回目に学習したモデルを2回目の学習に引き継ぎたいと思った.

しかしながら,その方法は公式ドキュメントには書いていなかったので,自分で調べて解決した.

結論としては,SequenceTagger.load()を使って,1回目のモデルを読み込み,それをtaggerとして用いた.

以下,1回目に学習したモデルを2回目の学習に引き継ぐコード.

from flair.data import Corpus
from flair.embeddings import TokenEmbeddings, WordEmbeddings, StackedEmbeddings
from flair.data import Sentence
from flair.models import SequenceTagger
from flair.embeddings import (
WordEmbeddings,
CharacterEmbeddings,
FlairEmbeddings,
BertEmbeddings,
)
from flair.data import Corpus
from flair.datasets import ColumnCorpus
from typing import List
from pathlib import Path
import sys
from docopt import docopt
def loadCorpus(data_folder):
# define columns
columns = {0: "text", 1: "ner"}
# init a corpus using column format, data folder and the names of the train, dev and test files
corpus: Corpus = ColumnCorpus(
data_folder,
columns,
train_file="train.tsv",
test_file="test.tsv",
dev_file="devel.tsv",
)
return corpus
# 1回目の学習
datapath = "/root/data/input/my-ner-data/"
# 1. get the corpus
corpus: Corpus = loadCorpus(datapath)
print(corpus)
# 2. what tag do we want to predict?
tag_type = "ner"
# 3. make the tag dictionary from the corpus
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
print(tag_dictionary.idx2item)
# 4. initialize embeddings
embedding_objects: List[TokenEmbeddings] = []
embedding_objects.append(CharacterEmbeddings())
embeddings: StackedEmbeddings = StackedEmbeddings(embeddings=embedding_objects)
# 5. initialize sequence tagger
tagger: SequenceTagger = SequenceTagger(
hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type=tag_type,
use_crf=True,
)
# 6. initialize trainer
from flair.trainers import ModelTrainer
resultpath = "/root/output/flair_test"
trainer: ModelTrainer = ModelTrainer(tagger, corpus)
# 7. start training
resultpath = Path(resultpath) / "tagger_results" / "char"
trainer.train(
str(resultpath),
learning_rate=0.1,
mini_batch_size=32,
max_epochs=5,
patience=5
)
# 2回目の学習
best_model = "/root/output/flair_test/tagger_results/char/best-model.pt"
# 1. get the corpus
corpus: Corpus = loadCorpus(datapath)
print(corpus)
# 2. what tag do we want to predict?
tag_type = "ner"
# 3. make the tag dictionary from the corpus
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
print(tag_dictionary.idx2item)
# 4. initialize embeddings
embedding_objects: List[TokenEmbeddings] = []
embedding_objects.append(CharacterEmbeddings())
embeddings: StackedEmbeddings = StackedEmbeddings(embeddings=embedding_objects)
# 5. initialize sequence tagger
# tagger: SequenceTagger = SequenceTagger(
#     hidden_size=256,
#     embeddings=embeddings,
#     tag_dictionary=tag_dictionary,
#     tag_type=tag_type,
#     use_crf=True,
# )
# 2回目のtaggerは,SequenceTagger.loadで読み込んだモデルを使う.
tagger = SequenceTagger.load(best_model)
# 6. initialize trainer
from flair.trainers import ModelTrainer
resultpath = "/root/output/flair_test"
trainer: ModelTrainer = ModelTrainer(tagger, corpus)
# 7. start training
resultpath = Path(resultpath) / "tagger_results" / "char"
trainer.train(
str(resultpath),
learning_rate=0.1,
mini_batch_size=32,
max_epochs=5,
patience=5
)
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