def test_train_resume_sequence_tagging_training(results_base_path,
tasks_base_path):
corpus_1 = flair.datasets.ColumnCorpus(data_folder=(tasks_base_path /
'fashion'),
column_format={
0: 'text',
2: 'ner',
})
corpus_2 = flair.datasets.GERMEVAL(base_path=tasks_base_path)
corpus = MultiCorpus([corpus_1, corpus_2])
tag_dictionary = corpus.make_tag_dictionary('ner')
embeddings = WordEmbeddings('turian')
model = SequenceTagger(hidden_size=64,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type='ner',
use_crf=False)
trainer = ModelTrainer(model, corpus)
trainer.train(results_base_path,
max_epochs=2,
shuffle=False,
checkpoint=True)
checkpoint = SequenceTagger.load_checkpoint(
(results_base_path / 'checkpoint.pt'))
trainer = ModelTrainer.load_from_checkpoint(checkpoint, corpus)
trainer.train(results_base_path,
max_epochs=2,
shuffle=False,
checkpoint=True)
shutil.rmtree(results_base_path)
def test_train_load_use_tagger_multicorpus(results_base_path, tasks_base_path):
corpus_1 = flair.datasets.ColumnCorpus(data_folder=(tasks_base_path /
'fashion'),
column_format={
0: 'text',
2: 'ner',
})
corpus_2 = flair.datasets.GERMEVAL(base_path=tasks_base_path)
corpus = MultiCorpus([corpus_1, corpus_2])
tag_dictionary = corpus.make_tag_dictionary('ner')
embeddings = WordEmbeddings('turian')
tagger = SequenceTagger(hidden_size=64,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type='ner',
use_crf=False)
trainer = ModelTrainer(tagger, corpus)
trainer.train(results_base_path,
learning_rate=0.1,
mini_batch_size=2,
max_epochs=2,
shuffle=False)
loaded_model = SequenceTagger.load((results_base_path / 'final-model.pt'))
sentence = Sentence('I love Berlin')
sentence_empty = Sentence(' ')
loaded_model.predict(sentence)
loaded_model.predict([sentence, sentence_empty])
loaded_model.predict([sentence_empty])
shutil.rmtree(results_base_path)
def test_train_resume_sequence_tagging_training(results_base_path, tasks_base_path):
corpus_1 = flair.datasets.ColumnCorpus(
data_folder=tasks_base_path / "fashion", column_format={0: "text", 2: "ner"}
)
corpus_2 = flair.datasets.GERMEVAL(base_path=tasks_base_path)
corpus = MultiCorpus([corpus_1, corpus_2])
tag_dictionary = corpus.make_tag_dictionary("ner")
embeddings = WordEmbeddings("turian")
model: SequenceTagger = SequenceTagger(
hidden_size=64,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type="ner",
use_crf=False,
)
trainer = ModelTrainer(model, corpus)
trainer.train(results_base_path, max_epochs=2, shuffle=False, checkpoint=True)
checkpoint = SequenceTagger.load_checkpoint(results_base_path / "checkpoint.pt")
trainer = ModelTrainer.load_from_checkpoint(checkpoint, corpus)
trainer.train(results_base_path, max_epochs=2, shuffle=False, checkpoint=True)
# clean up results directory
shutil.rmtree(results_base_path)
def run_evaluator(model_name: str, dataset_names: str):
corpus_list = []
# Dataset-related
for dataset in dataset_names.split((",")):
dataset_name, language = dataset.split("/")
preproc_fn = None
if dataset_name == "ajmc":
preproc_fn = prepare_ajmc_corpus
corpus_list.append(
NER_HIPE_2022(dataset_name=dataset_name,
language=language,
preproc_fn=preproc_fn,
add_document_separator=True))
corpora: MultiCorpus = MultiCorpus(corpora=corpus_list,
sample_missing_splits=False)
label_dictionary = corpora.make_label_dictionary(label_type="ner")
print("Label Dictionary:", label_dictionary.get_items())
model = SequenceTagger.load(model_name)
dev_result = model.evaluate(corpora.dev,
gold_label_type="ner",
mini_batch_size=8)
print(dev_result)
def multi_corpus(self,
output: Union[str, Path],
first_corpus: str = "germeval"):
data_dir = Path(Path(self.directory).parent, first_corpus)
first = self._load_corpus(data_dir)
second = self._load_corpus()
corpus = MultiCorpus([first, second])
tagger = self._train(output, corpus)
def test_train_load_use_tagger_multicorpus(results_base_path, tasks_base_path):
corpus_1 = flair.datasets.ColumnCorpus(data_folder=tasks_base_path /
"fashion",
column_format={
0: "text",
3: "ner"
})
corpus_2 = flair.datasets.NER_GERMAN_GERMEVAL(
base_path=tasks_base_path).downsample(0.1)
corpus = MultiCorpus([corpus_1, corpus_2])
tag_dictionary = corpus.make_label_dictionary("ner")
tagger: SequenceTagger = SequenceTagger(
hidden_size=64,
embeddings=turian_embeddings,
tag_dictionary=tag_dictionary,
tag_type="ner",
use_crf=False,
allow_unk_predictions=True,
)
# initialize trainer
trainer: ModelTrainer = ModelTrainer(tagger, corpus)
trainer.train(
results_base_path,
learning_rate=0.1,
mini_batch_size=2,
max_epochs=2,
shuffle=False,
)
del trainer, tagger, corpus
loaded_model: SequenceTagger = SequenceTagger.load(results_base_path /
"final-model.pt")
sentence = Sentence("I love Berlin")
sentence_empty = Sentence(" ")
loaded_model.predict(sentence)
loaded_model.predict([sentence, sentence_empty])
loaded_model.predict([sentence_empty])
del loaded_model
def test_train_load_use_tagger_multicorpus(results_base_path, tasks_base_path):
corpus_1 = flair.datasets.ColumnCorpus(data_folder=tasks_base_path /
"fashion",
column_format={
0: "text",
2: "ner"
})
corpus_2 = flair.datasets.GERMEVAL(base_path=tasks_base_path)
corpus = MultiCorpus([corpus_1, corpus_2])
tag_dictionary = corpus.make_tag_dictionary("ner")
embeddings = WordEmbeddings("turian")
tagger: SequenceTagger = SequenceTagger(
hidden_size=64,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type="ner",
use_crf=False,
)
# initialize trainer
trainer: ModelTrainer = ModelTrainer(tagger, corpus)
trainer.train(
results_base_path,
learning_rate=0.1,
mini_batch_size=2,
max_epochs=2,
shuffle=False,
)
loaded_model: SequenceTagger = SequenceTagger.load(results_base_path /
"final-model.pt")
sentence = Sentence("I love Berlin")
sentence_empty = Sentence(" ")
loaded_model.predict(sentence)
loaded_model.predict([sentence, sentence_empty])
loaded_model.predict([sentence_empty])
# clean up results directory
shutil.rmtree(results_base_path)
def test_multi_corpus(tasks_base_path):
corpus_1 = flair.datasets.ColumnCorpus(tasks_base_path / "germeval_14", column_format={0: "text", 2: "ner"})
corpus_2 = flair.datasets.ColumnCorpus(tasks_base_path / "fashion", column_format={0: "text", 2: "ner"})
# get two corpora as one
corpus = MultiCorpus([corpus_1, corpus_2])
assert len(corpus.train) == 8
assert len(corpus.dev) == 2
assert len(corpus.test) == 2
def run_experiment(seed, batch_size, epoch, learning_rate, hipe_datasets,
json_config):
# Config values
# Replace it with more Pythonic solutions later!
word_embedding = json_config["word_embedding"]
use_crf = json_config["use_crf"] if "use_crf" in json_config else False
# Set seed for reproducibility
set_seed(seed)
corpus_list = []
# Dataset-related
for dataset in hipe_datasets:
dataset_name, language = dataset.split("/")
corpus_list.append(
NER_HIPE_2022(dataset_name=dataset_name,
language=language,
add_document_separator=True))
print("Use CRF:", use_crf)
corpora: MultiCorpus = MultiCorpus(corpora=corpus_list,
sample_missing_splits=False)
label_dictionary = corpora.make_label_dictionary(label_type="ner")
print("Label Dictionary:", label_dictionary.get_items())
# FastText Embeddings
embeddings = FastTextEmbeddings(embeddings=word_embedding)
tagger: SequenceTagger = SequenceTagger(
hidden_size=256,
embeddings=embeddings,
tag_dictionary=label_dictionary,
tag_type="ner",
use_crf=use_crf,
)
# Trainer
trainer: ModelTrainer = ModelTrainer(tagger, corpora)
datasets = "-".join([dataset for dataset in hipe_datasets])
trainer.train(
f"hipe2022-flert-we-trainer-{datasets}-{word_embedding}-bs{batch_size}-wsFalse-e{epoch}-lr{learning_rate}-crf{use_crf}-{seed}",
mini_batch_size=batch_size,
mini_batch_chunk_size=2,
patience=3,
max_epochs=epoch,
shuffle=True,
learning_rate=learning_rate,
)
# Finally, print model card for information
tagger.print_model_card()
def test_train_resume_tagger(results_base_path, tasks_base_path):
corpus_1 = flair.datasets.ColumnCorpus(data_folder=tasks_base_path /
"fashion",
column_format={
0: "text",
3: "ner"
})
corpus_2 = flair.datasets.NER_GERMAN_GERMEVAL(
base_path=tasks_base_path).downsample(0.1)
corpus = MultiCorpus([corpus_1, corpus_2])
tag_dictionary = corpus.make_label_dictionary("ner")
model: SequenceTagger = SequenceTagger(
hidden_size=64,
embeddings=turian_embeddings,
tag_dictionary=tag_dictionary,
tag_type="ner",
use_crf=False,
)
# train model for 2 epochs
trainer = ModelTrainer(model, corpus)
trainer.train(results_base_path,
max_epochs=2,
shuffle=False,
checkpoint=True)
del model
# load the checkpoint model and train until epoch 4
checkpoint_model = SequenceTagger.load(results_base_path / "checkpoint.pt")
trainer.resume(model=checkpoint_model, max_epochs=4)
# clean up results directory
del trainer
# 2. what tag do we want to predict?
tag_type = 'pos'
# 3. make the tag dictionary from the corpus
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
# initialize embeddings
embedding_types: List[TokenEmbeddings] = [
WordEmbeddings('extvec'),
FlairEmbeddings('news-forward'),
FlairEmbeddings('news-backward'),
]
embeddings: StackedEmbeddings = StackedEmbeddings(embeddings=embedding_types)
tagger: SequenceTagger = SequenceTagger(hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type=tag_type)
trainer: ModelTrainer = ModelTrainer(tagger, corpus)
multi_corpus = MultiCorpus([english_corpus, german_corpus, dutch_corpus])
result = trainer.train('resources/taggers/example-pos',
train_with_dev=True,
embeddings_storage_mode='cpu',
mini_batch_size=256,
max_epochs=max_epochs)
def run_experiment(seed, batch_size, epoch, learning_rate, hipe_datasets,
json_config):
# Config values
# Replace it with more Pythonic solutions later!
hf_model = json_config["hf_model"]
context_size = json_config["context_size"]
layers = json_config["layers"] if "layers" in json_config else "-1"
use_crf = json_config["use_crf"] if "use_crf" in json_config else False
# Set seed for reproducibility
set_seed(seed)
corpus_list = []
# Dataset-related
for dataset in hipe_datasets:
dataset_name, language = dataset.split("/")
current_corpus = NER_HIPE_2022(dataset_name=dataset_name,
language=language,
add_document_separator=True)
for split in ["train", "dev"]:
kb_data = []
print(f"Loading KB contexts for {dataset}...")
with open(f"kb_data/ajmc/{language}/{language}_{split}.jsonl",
"rt") as f_p:
for line in f_p:
kb_data.append(line)
corpus_split = current_corpus.train if split == "train" else current_corpus.dev
for index, sent in enumerate(corpus_split):
jsonl = json.loads(kb_data[index])
kb_context = " ".join(jsonl["contexts"]).split(" ")
sent.kb_context = kb_context
corpus_list.append(current_corpus)
if context_size == 0:
context_size = False
print("FLERT Context:", context_size)
print("Layers:", layers)
print("Use CRF:", use_crf)
corpora: MultiCorpus = MultiCorpus(corpora=corpus_list,
sample_missing_splits=False)
label_dictionary = corpora.make_label_dictionary(label_type="ner")
print("Label Dictionary:", label_dictionary.get_items())
# Embeddings
embeddings = KBTransformerEmbeddings(
model=hf_model,
layers=layers,
subtoken_pooling="first",
fine_tune=True,
use_context=context_size,
)
tagger: SequenceTagger = SequenceTagger(
hidden_size=256,
embeddings=embeddings,
tag_dictionary=label_dictionary,
tag_type="ner",
use_crf=use_crf,
use_rnn=False,
reproject_embeddings=False,
)
# Trainer
trainer: ModelTrainer = ModelTrainer(tagger, corpora)
datasets = "-".join([dataset for dataset in hipe_datasets])
trainer.fine_tune(
f"hipe2022-flert-fine-tune-kb-{datasets}-{hf_model}-bs{batch_size}-ws{context_size}-e{epoch}-lr{learning_rate}-layers{layers}-crf{use_crf}-{seed}",
learning_rate=learning_rate,
mini_batch_size=batch_size,
max_epochs=epoch,
shuffle=True,
embeddings_storage_mode='none',
weight_decay=0.,
use_final_model_for_eval=False,
)
# Finally, print model card for information
tagger.print_model_card()
# 2. initialize embeddings
from flair.embeddings import WordEmbeddings, FlairEmbeddings, StackedEmbeddings
embedding_types = [
WordEmbeddings("pubmed"),
FlairEmbeddings("pubmed-forward"),
FlairEmbeddings("pubmed-backward"),
]
embeddings = StackedEmbeddings(embeddings=embedding_types)
# 3. Initialize corpus
# We also train on the test portions of the corpora, because we evaluate on held-out corpora
from flair.data import MultiCorpus
from torch.utils.data import ConcatDataset
corpus = MultiCorpus(GENE_CORPORA)
corpus._train = ConcatDataset([corpus._train, corpus._test])
# 4. Initialize sequence tagger
from flair.models import SequenceTagger
tag_dictionary = corpus.make_tag_dictionary(tag_type="ner")
tagger = SequenceTagger(
hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type="ner",
use_crf=True,
locked_dropout=0.5
)
def load_corpora(tasks: List[Union[NLPTask, str]],
base_path: Path = None) -> MultiCorpus:
return MultiCorpus([
NLPTaskDataFetcher.load_corpus(task, base_path) for task in tasks
])
print('-' * 150, flush=True)
print(" - Domain: ", domain, flush=True)
print(" - Corpus Train Size: ", len(corpus.train), flush=True)
print(" - Corpus Dev Size: ", len(corpus.dev), flush=True)
print(" - Corpus Test Size: ", len(corpus.test), flush=True)
print('-' * 150, flush=True)
# initialize embeddings
embedding_types: List[TokenEmbeddings] = [
WordEmbeddings('extvec'),
FlairEmbeddings('news-forward'),
FlairEmbeddings('news-backward'),
]
embeddings: StackedEmbeddings = StackedEmbeddings(embeddings=embedding_types)
multi_all_corpus = MultiCorpus(ALL_POS_ALL_CORPUS.values())
# 3. make the tag dictionary from the corpus
tag_dictionary = multi_all_corpus.make_tag_dictionary(tag_type=tag_type)
print('-' * 50,
'\nTag_dictionary size: ',
len(tag_dictionary),
'\n',
'-' * 50,
flush=True)
# initialize sequence tagger
tagger: SequenceTagger = SequenceTagger(hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type=tag_type)
for target_domain in ALL_POS_DOMAINS:
def run_experiment(seed, batch_size, epoch, learning_rate, hipe_datasets,
json_config):
# Config values
# Replace it with more Pythonic solutions later!
hf_model = json_config["hf_model"]
context_size = json_config["context_size"]
layers = json_config["layers"] if "layers" in json_config else "-1"
use_crf = json_config["use_crf"] if "use_crf" in json_config else False
additional_hipe_datasets = json_config[
"additional_hipe_datasets"] if "additional_hipe_datasets" in json_config else None
label_name_map = json_config[
"label_name_map"] if "label_name_map" in json_config else None
# Set seed for reproducibility
set_seed(seed)
corpus_list = []
# Dataset-related
for dataset in hipe_datasets:
dataset_name, language = dataset.split("/")
preproc_fn = None
if dataset_name == "ajmc":
preproc_fn = prepare_ajmc_corpus
corpus_list.append(
NER_HIPE_2022(dataset_name=dataset_name,
language=language,
preproc_fn=preproc_fn,
add_document_separator=True))
if additional_hipe_datasets and label_name_map:
# Special case: do not use Dev data from additional datasets
# This makes evaluation and comparison much more easier!
for dataset in additional_hipe_datasets:
dataset_name, language = dataset.split("/")
preproc_fn = None
if dataset_name == "hipe2020":
print("Using own HIPE-2020 Preprocessing function.")
print(
"Please make sure that Flair Datasets folder was cleaned before!"
)
preproc_fn = prepare_clef_2020_corpus
additional_corpus = NER_HIPE_2022(dataset_name=dataset_name,
label_name_map=label_name_map,
language=language,
add_document_separator=True,
preproc_fn=preproc_fn)
additional_corpus._dev = []
corpus_list.append(additional_corpus)
if context_size == 0:
context_size = False
print("FLERT Context:", context_size)
print("Layers:", layers)
print("Use CRF:", use_crf)
corpora: MultiCorpus = MultiCorpus(corpora=corpus_list,
sample_missing_splits=False)
label_dictionary = corpora.make_label_dictionary(label_type="ner")
print("Label Dictionary:", label_dictionary.get_items())
# Embeddings
embeddings = TransformerWordEmbeddings(
model=hf_model,
layers=layers,
subtoken_pooling="first",
fine_tune=True,
use_context=context_size,
)
tagger: SequenceTagger = SequenceTagger(
hidden_size=256,
embeddings=embeddings,
tag_dictionary=label_dictionary,
tag_type="ner",
use_crf=use_crf,
use_rnn=False,
reproject_embeddings=False,
)
# Trainer
trainer: ModelTrainer = ModelTrainer(tagger, corpora)
datasets = "-".join([dataset for dataset in hipe_datasets])
trainer.fine_tune(
f"hipe2022-flert-fine-tune-{datasets}-{hf_model}-bs{batch_size}-ws{context_size}-e{epoch}-lr{learning_rate}-layers{layers}-crf{use_crf}-{seed}",
learning_rate=learning_rate,
mini_batch_size=batch_size,
max_epochs=epoch,
shuffle=True,
embeddings_storage_mode='none',
weight_decay=0.,
use_final_model_for_eval=False,
)
# Finally, print model card for information
tagger.print_model_card()
def run_experiment(seed, batch_size, epoch, learning_rate, hipe_datasets,
json_config):
# Config values
# Replace it with more Pythonic solutions later!
best_model = json_config["best_model"]
context_size = json_config["context_size"]
layers = json_config["layers"] if "layers" in json_config else "-1"
use_crf = json_config["use_crf"] if "use_crf" in json_config else False
# Set seed for reproducibility
set_seed(seed)
corpus_list = []
# Dataset-related
for dataset in hipe_datasets:
dataset_name, language = dataset.split("/")
preproc_fn = None
if dataset_name == "ajmc":
preproc_fn = prepare_ajmc_corpus
corpus_list.append(
NER_HIPE_2022(dataset_name=dataset_name,
language=language,
preproc_fn=preproc_fn,
add_document_separator=True))
if context_size == 0:
context_size = False
print("FLERT Context:", context_size)
print("Layers:", layers)
print("Use CRF:", use_crf)
corpora: MultiCorpus = MultiCorpus(corpora=corpus_list,
sample_missing_splits=False)
label_dictionary = corpora.make_label_dictionary(label_type="ner")
print("Label Dictionary:", label_dictionary.get_items())
print("Loading model from stage 1:", best_model)
tagger: SequenceTagger = SequenceTagger.load(best_model)
# Trainer
trainer: ModelTrainer = ModelTrainer(tagger, corpora)
datasets = "-".join([dataset for dataset in hipe_datasets])
best_model_name = best_model.replace("/", "_")
trainer.fine_tune(
f"hipe2022-flert-fine-tune-multistage-{datasets}-{best_model_name}-bs{batch_size}-ws{context_size}-e{epoch}-lr{learning_rate}-layers{layers}-crf{use_crf}-{seed}",
learning_rate=learning_rate,
mini_batch_size=batch_size,
max_epochs=epoch,
shuffle=True,
embeddings_storage_mode='none',
weight_decay=0.,
use_final_model_for_eval=False,
)
# Finally, print model card for information
tagger.print_model_card()
import flair
from flair.data import Corpus
#from flair.datasets import TREC_6
from flair.models import SimpleSequenceTagger
from flair.trainers import ModelTrainer
from flair.embeddings import WordEmbeddings
from flair.data import Sentence
from flair.data import MultiCorpus
from flair.datasets import UD_ENGLISH, UD_GERMAN
# 1. get the corpora - English and German UD
corpus: MultiCorpus = MultiCorpus([UD_ENGLISH(), UD_GERMAN()]).downsample(0.1)
# 2. what tag do we want to predict?
tag_type = 'upos'
# 3. make the tag dictionary from the corpus
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
print(tag_dictionary)
#4 embedding
glove_embedding = WordEmbeddings('glove')
#5 sequence tagger
tagger = SimpleSequenceTagger(
columns = {0: 'text', 1: 'ner'}
data_folder = '../'
corpus1: TaggedCorpus = NLPTaskDataFetcher.load_column_corpus(
data_folder,
columns,
train_file="de-da-te-ta.10E-4percent.conll.train.txt",
test_file="de-da-te-ta.10E-4percent.conll.test.txt",
dev_file="de-da-te-ta.10E-4percent.conll.dev.txt")
corpus2: TaggedCorpus = NLPTaskDataFetcher.load_column_corpus(
data_folder,
columns,
train_file="de-da-te-ta.10E-4percent.conll.84max.train.txt",
test_file="de-da-te-ta.10E-4percent.conll.84max.test.txt",
dev_file="de-da-te-ta.10E-4percent.conll.84max.dev.txt")
corpus = MultiCorpus([corpus1, corpus2])
custom_embedding = WordEmbeddings(
'../../glove/GloVe/vectors_converted_to_gensim.gensim')
#bert_embedding = BertEmbeddings('bert-embedding-files/')
word_embeddings = StackedEmbeddings([custom_embedding, WordEmbeddings('tr')])
search_space = SearchSpace()
search_space.add(Parameter.EMBEDDINGS, hp.choice, options=[word_embeddings])
#search_space.add(Parameter.HIDDEN_SIZE, hp.choice, options=[32, 64, 128, 256, 512])
search_space.add(Parameter.HIDDEN_SIZE, hp.choice, options=[256])
search_space.add(Parameter.RNN_LAYERS, hp.choice, options=[2])
#search_space.add(Parameter.DROPOUT, hp.uniform, low=0.0, high=0.5)
search_space.add(Parameter.LEARNING_RATE,
hp.choice,
options=[0.05, 0.1, 0.15, 0.2, 0.25])
from flair.data import TaggedCorpus, MultiCorpus
from flair.data_fetcher import NLPTaskDataFetcher
from flair.embeddings import TokenEmbeddings, WordEmbeddings, StackedEmbeddings, BertEmbeddings
from typing import List
from flair.data import Dictionary
import flair, torch
flair.device = torch.device('cpu')
columns = {0: 'text', 1: 'ner'}
data_folder = '../'
corpus1: TaggedCorpus = NLPTaskDataFetcher.load_column_corpus(data_folder, columns, train_file="de-da-te-ta.10E-4percent.conll.train.txt", test_file="de-da-te-ta.10E-4percent.conll.test.txt", dev_file="de-da-te-ta.10E-4percent.conll.dev.txt")
corpus2: TaggedCorpus = NLPTaskDataFetcher.load_column_corpus(data_folder, columns, train_file="de-da-te-ta.10E-4percent.conll.84max.train.txt", test_file="de-da-te-ta.10E-4percent.conll.84max.test.txt", dev_file="de-da-te-ta.10E-4percent.conll.84max.dev.txt")
corpus = MultiCorpus([corpus1, corpus2])
tag_type = 'ner'
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
#tag_dictionary: Dictionary = Dictionary.load('../vocab/m.model')
glove_embedding = WordEmbeddings('../../glove/GLOVE/GloVe/vectors.gensim')
word2vec_embedding = WordEmbeddings('../../huawei_w2v/vector.gensim')
#bert_embedding = BertEmbeddings('../bert_pretraining/pretraining_outputs/pretraining_output_batch_size_32')
embedding_types: List[TokenEmbeddings] = [WordEmbeddings('tr'), glove_embedding, word2vec_embedding]
#embedding_types: List[TokenEmbeddings] = [custom_embedding]
embeddings: StackedEmbeddings = StackedEmbeddings(embeddings=embedding_types)
from flair.models import SequenceTagger
tagger: SequenceTagger = SequenceTagger(hidden_size=256, embeddings=embeddings, tag_dictionary=tag_dictionary, tag_type=tag_type, use_crf=True, use_rnn=True, rnn_layers=2)
from flair.trainers import ModelTrainer
