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flair_train.py
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flair_train.py
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from typing import List
import pandas as pd
try:
from flair.data import Corpus, Sentence
from flair.datasets import ColumnCorpus
from flair.embeddings import (
TokenEmbeddings,
WordEmbeddings,
StackedEmbeddings,
FlairEmbeddings,
TransformerWordEmbeddings,
)
from flair.models import SequenceTagger
from flair.trainers import ModelTrainer
except ImportError:
print("Flair is not installed")
from presidio_evaluator import InputSample
from os import path
class FlairTrainer:
"""
Helper class for training Flair models
"""
@staticmethod
def to_flair_row(text: str, pos: str, label: str) -> str:
"""
Turn text, part of speech and label into one row.
:return: str
"""
return "{} {} {}".format(text, pos, label)
def to_flair(self, df: pd.DataFrame, outfile: str = "flair_train.txt") -> None:
"""Translate a pd.DataFrame to a flair dataset."""
sentence = 0
flair = []
for row in df.itertuples():
if row.sentence != sentence:
sentence += 1
flair.append("")
else:
flair.append(self.to_flair_row(row.text, row.pos, row.label))
if outfile:
with open(outfile, "w", encoding="utf-8") as f:
for item in flair:
f.write("{}\n".format(item))
def create_flair_corpus(
self, train_samples_path, test_samples_path, val_samples_path
):
"""
Create a flair Corpus object and saive it to train, test, validation files.
:param train_samples_path: Path to train samples
:param test_samples_path: Path to test samples
:param val_samples_path: Path to validation samples
:return:
"""
if not path.exists("flair_train.txt"):
train_samples = InputSample.read_dataset_json(train_samples_path)
train_tagged = [sample for sample in train_samples if len(sample.spans) > 0]
print(
f"Kept {len(train_tagged)} train samples after removal of non-tagged samples"
)
train_data = InputSample.create_conll_dataset(train_tagged)
self.to_flair(train_data, outfile="flair_train.txt")
if not path.exists("flair_test.txt"):
test_samples = InputSample.read_dataset_json(test_samples_path)
test_data = InputSample.create_conll_dataset(test_samples)
self.to_flair(test_data, outfile="flair_test.txt")
if not path.exists("flair_val.txt"):
val_samples = InputSample.read_dataset_json(val_samples_path)
val_data = InputSample.create_conll_dataset(val_samples)
self.to_flair(val_data, outfile="flair_val.txt")
@staticmethod
def read_corpus(data_folder: str, in_memory=True):
"""
Read Flair Corpus object.
:param data_folder: Path with files
:return: Corpus object
"""
columns = {0: "text", 1: "pos", 2: "ner"}
corpus = ColumnCorpus(
data_folder,
columns,
train_file="flair_train.txt",
test_file="flair_val.txt",
dev_file="flair_test.txt",
in_memory=in_memory,
)
return corpus
@staticmethod
def train_with_flair_embeddings(corpus, checkpoint_path=""):
"""
Train a Flair model
:param corpus: Corpus object
:return:
"""
print("Corpus: ", corpus)
# 2. what tag do we want to predict?
tag_type = "ner"
# 3. make the label dictionary from the corpus
tag_dictionary = corpus.make_label_dictionary(
label_type=tag_type, add_unk=False)
print("Tag dictionary: ", tag_dictionary)
# 4. initialize embeddings
embedding_types: List[TokenEmbeddings] = [
WordEmbeddings("glove"),
FlairEmbeddings("news-forward"),
FlairEmbeddings("news-backward"),
]
embeddings: StackedEmbeddings = StackedEmbeddings(embeddings=embedding_types)
# 5. initialize sequence tagger
tagger = SequenceTagger(
hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type=tag_type,
use_crf=True,
)
# 6. initialize trainer
trainer = ModelTrainer(tagger, corpus)
if checkpoint_path:
trained_model = SequenceTagger.load(checkpoint_path)
trainer.resume(
model=trained_model,
)
else:
path = "resources/taggers/presidio-ner",
trainer.train(
path,
learning_rate=0.1,
mini_batch_size=32,
max_epochs=150,
checkpoint=True,
)
sentence = Sentence("I am from Jerusalem")
# run NER over sentence
tagger.predict(sentence)
print(sentence)
print("The following NER tags are found:")
# iterate over entities and print
for entity in sentence.get_spans("ner"):
print(entity)
@staticmethod
def train_with_transformers(corpus, checkpoint_path=""):
"""
Train a Flair model
:param corpus: Corpus object
:return:
"""
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_label_dictionary(
label_type=tag_type, add_unk=False)
print(tag_dictionary)
# 4. initialize fine-tuneable transformer embeddings WITH document context
embedding_types: List[TokenEmbeddings] = [TransformerWordEmbeddings(model="xlm-roberta-large",
layers="-1",
subtoken_pooling="first",
fine_tune=True,
use_context=True,
)]
embeddings: StackedEmbeddings = StackedEmbeddings(embeddings=embedding_types)
# 5. initialize bare-bones sequence tagger (no CRF, no RNN, no reprojection)
tagger = SequenceTagger(hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type='ner',
use_crf=False,
use_rnn=False,
reproject_embeddings=False,
)
# 6. initialize trainer
trainer: ModelTrainer = ModelTrainer(tagger, corpus)
if checkpoint_path:
trained_model = SequenceTagger.load(checkpoint_path)
trainer.resume(model=trained_model)
# 7. run fine-tuning
else:
trainer.fine_tune('resources/taggers/presidio-ner',
learning_rate=5.0e-6,
mini_batch_size=4,
max_epochs=20,
mini_batch_chunk_size=1, # remove this parameter to speed up computation if you have a big GPU
checkpoint=True,
)
sentence = Sentence("I am from Jerusalem")
# run NER over sentence
tagger.predict(sentence)
print(sentence)
print("The following NER tags are found:")
# iterate over entities and print
for entity in sentence.get_spans("ner"):
print(entity)
if __name__ == "__main__":
train_samples = "../../data/train_Dec-19-2021.json"
test_samples = "../../data/test_Dec-19-2021.json"
val_samples = "../../data/validation_Dec-19-2021.json"
trainer = FlairTrainer()
trainer.create_flair_corpus(train_samples, test_samples, val_samples)
corpus = trainer.read_corpus("")
trainer.train_with_flair_embeddings(corpus)
trainer.train_with_transformers(corpus)