def main(base_path, output_dir, nb_epochs):
# parser = argparse.ArgumentParser()
# parser.add_argument("--data_dir", default='./', type=str, required=True, help="The parent dir of inpu data, must contain folder name `conll_03`")
# parser.add_argument("--output_dir", default=None, required=True, help="The output directory where is going to store the trained model")
# parser.add_argument("--train_epochs", default=3, type=int, required=True, help="Number of epochs to train")
# args = parser.parse_args()
# base_path = args.data_dir
corpus: Corpus = CONLL_03(base_path=base_path)
tag_type = 'ner'
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
embedding_types: List[TokenEmbeddings] = [
WordEmbeddings('glove'),
PooledFlairEmbeddings('news-forward', pooling='min'),
PooledFlairEmbeddings('news-backward', pooling='min'),
]
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)
# output_dir = args.output_dir
# nb_epochs = args.train_epochs
# output_dir =
# nb_epochs = 10
trainer.train(output_dir, train_with_dev=False,
max_epochs=nb_epochs) # 150
def main(data_dir, model_path, result_file):
# parser = argparse.ArgumentParser()
# parser.add_argument("--data_dir", default='./', type=str, help="The parent dir of input data, should include folder named `conll_03` ")
# parser.add_argument("--model_dir", default=None, type=str, required=True, help="The model directory where model chekpoints stored")
# parser.add_argument("--result_file", default='dev.tsv', type=str, required=True, help="The name of prediction file, default save in current dir")
# parser.add_argument("--eval_on", default='dev', type=str, required=True, help="Whether to eval on dev set or test set")
# args = parser.parse_args()
# model_path = args.model_dir
model = SequenceTagger.load(model_path + '/final-model.pt')
# corpus: Corpus = CONLL_03(base_path=args.data_dir)
corpus: Corpus = CONLL_03(base_path=data_dir)
testdata = corpus.dev
# test_result, test_loss = model.evaluate(testdata, out_path=args.result_file)
test_result, test_loss = model.evaluate(testdata, out_path=result_file)
result_line = f"\t{test_loss}\t{test_result.log_line}"
# main score is micro averaged f1 score
# result line is precision, recall, micro averaged score
print(f"TEST : loss {test_loss} - score {round(test_result.main_score, 4)}")
print(f"TEST RESULT: {result_line}")
print(test_result.detailed_results)
def flairInfer(model_path, test_or_train):
from flair.data import Sentence
from flair.models import SequenceTagger
from flair.data import Corpus
from flair.datasets import CONLL_03
from flair.datasets import ColumnCorpus
model = SequenceTagger.load(model_path + '/final-model.pt')
data_dir = '../GmbDataExperimentation/processed_data/1500_data'
try:
corpus: Corpus = CONLL_03(base_path=data_dir)
except:
pass
columns = {0: 'text', 1: 'ner'}
corpus: Corpus = ColumnCorpus(data_dir, columns)
if test_or_train == 'train':
testdata = corpus.train
result_file = data_dir + '/train.tsv'
else:
testdata = corpus.test
result_file = data_dir + '/test.tsv'
test_result, test_loss = model.evaluate(testdata, out_path=result_file)
result_line = f"\t{test_loss}\t{test_result.log_line}"
print(
f"TEST : loss {test_loss} - score {round(test_result.main_score, 4)}")
print(f"TEST RESULT : {result_line}")
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--data_dir",
default='./',
type=str,
help=
"The parent dir of input data, should include folder named `conll_03` ")
parser.add_argument("--model_dir",
default=None,
type=str,
required=True,
help="The model directory where model chekpoints stored")
parser.add_argument(
"--result_file",
default='dev.tsv',
type=str,
required=True,
help=
"The name of prediction file, default is in the same dir of script file")
parser.add_argument("--eval_on",
default='dev',
type=str,
required=True,
help="Whether to eval on dev set or test set")
args = parser.parse_args()
model_path = args.model_dir
data_dir = args.data_dir
model = SequenceTagger.load(model_path + '/final-model.pt')
try:
corpus: Corpus = CONLL_03(base_path=data_dir)
except:
pass
columns = {0: 'text', 1: 'ner'}
corpus: Corpus = ColumnCorpus(data_dir, columns)
if args.eval_on == 'dev':
testdata = corpus.dev
elif args.eval_on == 'test':
testdata = corpus.test
elif args.eval_on == 'train':
print('You are evaluating on training set!')
testdata = corpus.train
else:
raise ValueError("Invalid argument, must specify evaluation on dev or test")
test_result, test_loss = model.evaluate(testdata, out_path=args.result_file)
result_line = f"\t{test_loss}\t{test_result.log_line}"
# main score is micro averaged f1 score
# result line is precision, recall, micro averaged score
print(f"TEST : loss {test_loss} - score {round(test_result.main_score, 4)}")
print(f"TEST RESULT: {result_line}")
print(test_result.detailed_results)
def trainNER(data_dir, model_dir):
parser = argparse.ArgumentParser()
parser.add_argument("--model",
default='bert-base-cased',
type=str,
required=True,
help="The pretrained model to produce embeddings")
args = parser.parse_args()
model = args.model
# pdb.set_trace()
try:
corpus: Corpus = CONLL_03(base_path=data_dir + '/')
except FileNotFoundError:
columns = {0: 'text', 1: 'ner'}
corpus: Corpus = ColumnCorpus(data_dir, columns)
corpus.filter_empty_sentences()
tag_type = 'ner'
# tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
tag_dictionary = corpus.make_label_dictionary('ner')
print(tag_dictionary.get_items())
stats = corpus.obtain_statistics()
print(stats)
# ['<unk>', 'O', 'B-DEVICE', 'I-DEVICE', 'B-TREE', 'I-TREE', 'B-APPLICATION', 'I-APPLICATION', 'B-LOCATION', 'I-LOCATION', '<START>', '<STOP>']
# pdb.set_trace()
embedding_types: List[TokenEmbeddings] = [
WordEmbeddings('glove'),
TransformerWordEmbeddings(
model=model,
layers='0', # dtype: str
pooling_operation='first_last',
use_scalar_mix=False,
batch_size=16,
fine_tune=False,
allow_long_sentences=False)
]
embeddings: StackedEmbeddings = StackedEmbeddings(
embeddings=embedding_types)
# biLSTM + CRF
# tagger: SequenceTagger = SequenceTagger(hidden_size=256,
# embeddings=embeddings,
# tag_dictionary=tag_dictionary,
# tag_type=tag_type)
model_path = '/home/carolyn/Projects/mygit/Flair-NER/exprmt-20201120/conll_frac/10ptdata/models-5e-20201124/final-model.pt'
tagger: SequenceTagger = SequenceTagger.load(model_path)
trainer: ModelTrainer = ModelTrainer(tagger, corpus)
trainer.train(model_dir, train_with_dev=False, max_epochs=10) # 150
def train_flair(self):
# Flair Model Initialisation and Training
# # 1. get the corpus
# corpus: Corpus = ColumnCorpus(os.path.join(os.getcwd(), 'results', '10'),
# {0: 'text', 1: 'ner'},
# train_file='train.txt',
# test_file='test.txt',
# dev_file='valid.txt',
# column_delimiter=' ')
corpus: Corpus = CONLL_03(
base_path=os.path.join(os.getcwd(), 'results', '10'))
corpus.dev_file = 'valid.txt' # rather than 'dev.txt'
# 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)
# initialize embeddings
embedding_types: List[TokenEmbeddings] = [
# GloVe embeddings
WordEmbeddings('glove'),
# contextual string embeddings, forward
PooledFlairEmbeddings('news-forward', pooling='min'),
# contextual string embeddings, backward
PooledFlairEmbeddings('news-backward', pooling='min'),
]
embeddings: StackedEmbeddings = StackedEmbeddings(
embeddings=embedding_types)
# initialize sequence tagger
tagger: SequenceTagger = SequenceTagger(hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type=tag_type)
# initialize trainer
trainer: ModelTrainer = ModelTrainer(tagger, corpus)
results = trainer.train(os.path.join(os.getcwd(), 'results', '10',
'tagger'),
train_with_dev=False,
max_epochs=50)
print(results)
def testModel(model_dir, test_sent=None, test_file_dir=None):
"""
model_dir: directory contains 'final_model.pt'
test_sent: one sentence to test
test_file: one file of sentences to test
"""
if test_sent and test_file_dir:
raise Exception(
"Argument conflicts, only one type of testing method is allowed.")
elif not test_sent and not test_file_dir:
raise Exception(
"Argument invalid, at least one testing method is required")
model_path = model_dir + '/final-model.pt'
model = SequenceTagger.load(model_path)
if test_sent:
print('Predicting in singular mode')
test_sent = Sentence(test_sent)
model.predict(test_sent)
print(test_sent.to_tagged_string())
if test_file_dir:
print('Predicting in plural mode')
try:
corpus = CONLL_03(base_path=test_file_dir + '/')
test_data = corpus.test
except:
try:
columns = {0: 'text', 1: 'ner'}
corpus = ColumnCorpus(test_file_dir, columns)
test_data = corpus.test
except AttributeError:
raise Exception(
'Directory must contain `test.txt` file, one column `text` the other `ner`'
)
test_result, test_loss = model.evaluate(test_data,
out_path=test_file_dir +
'/test_20201210.tsv')
result_line = f"\t{test_loss}\t{test_result.log_line}"
print(
f"TEST : loss {test_loss} - score {round(test_result.main_score, 4)}"
)
print(f"TEST RESULT : {result_line}")
print('end')
tagger = TARSSequenceTagger2.load(
"resources/v3/moviecomplex-long/final-model.pt")
flair.set_seed(3)
label_name_map = {
"LOC": "Location",
"PER": "Person",
"ORG": "Organization",
"MISC": "Miscellaneous"
}
print(label_name_map)
corpus = CONLL_03(
tag_to_bioes=None,
tag_to_bio2="ner",
label_name_map=label_name_map,
base_path="/vol/fob-vol7/mi19/harnisph/studienprojekt-dokumentation")
corpus = corpus.downsample(0.1)
tag_type = "ner"
tag_dictionary = corpus.make_label_dictionary(tag_type)
tagger.add_and_switch_to_new_task("zeroshot-moviecomplex-long-to-conll3",
tag_dictionary=tag_dictionary,
tag_type=tag_type)
result, eval_loss = tagger.evaluate(corpus.test)
print(result.main_score)
print(result.log_header)
print(result.log_line)
print(result.detailed_results)
print(eval_loss)
# flair.set_seed(2)
# flair.set_seed(3)
from flair.datasets import CONLL_03
from mapping import (twitter_ner_mapped, onto_ner_mapped, wikigold_ner_mapped,
webpages_ner_mapped)
dataset_name = "conll3"
for seed in [1, 2, 3]:
flair.set_seed(123)
if dataset_name == "onto_ner":
corpus = onto_ner_mapped()
elif dataset_name == "conll3":
corpus = CONLL_03()
elif dataset_name == "wikipedia":
corpus = wikigold_ner_mapped()
elif dataset_name == "webpages":
corpus = webpages_ner_mapped()
elif dataset_name == "twitter":
corpus = twitter_ner_mapped()
flair.set_seed(seed)
# 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)
def load_corpus(corpus_name,
col_idx,
text_idx,
tag_type='ner',
downsample_perc=1.0,
name_train=None,
name_dev=None,
name_test=None,
verbose=False):
"""
Loads a corpus with a given name.
Optionally performs downsampling of the data.
Parameters:
corpus_name (str): name of the corpus used to load proper embeddings
col_idx (int): index of the column's tag
text_idx (int): index of the text's tag
tag_type (str): type of the tag to load
downsample_rate (float): downsample rate (1.0 = full corpus)
name_train (str): name of a file containing the train set
name_dev (str): name of a file containing the development set
name_test (str): name of a file containing the test set
Returns:
ColumnCorpus: the loaded corpus
"""
from pathlib import Path
data_dir = f'resources/tasks/'
if corpus_name in ["conll03_en"]:
from flair.datasets import CONLL_03
corpus = CONLL_03(base_path=Path(data_dir), tag_to_bioes=tag_type)
elif corpus_name in ["conll03_de"]:
from flair.datasets import CONLL_03_GERMAN
corpus = CONLL_03_GERMAN(base_path=Path(data_dir),
tag_to_bioes=tag_type)
elif corpus_name in ["germeval"]:
from flair.datasets import GERMEVAL
corpus = GERMEVAL(base_path=Path(data_dir), tag_to_bioes=tag_type)
else:
corpus_dir = f"{data_dir}{corpus_name}"
if not os.path.exists(corpus_dir):
log.error(f"Data directory '{corpus_dir}' does not exists!")
exit(EXIT_FAILURE)
from flair.datasets import ColumnCorpus
columns = {text_idx: 'text', col_idx: tag_type}
train_set = None if name_train is None else f'{name_train}'
dev_set = None if name_dev is None else f'{name_dev}'
test_set = None if name_test is None else f'{name_test}'
corpus: ColumnCorpus = ColumnCorpus(corpus_dir,
columns,
train_file=train_set,
test_file=test_set,
dev_file=dev_set,
tag_to_bioes=tag_type)
if downsample_perc >= 0.0 and downsample_perc < 1.0:
corpus.downsample(downsample_perc)
if verbose:
log.info(corpus.obtain_statistics(tag_type=tag_type))
log.info("'{}' function finished!".format(sys._getframe().f_code.co_name))
return corpus
# -*- coding: utf-8 -*-
from flair.datasets import CONLL_03, DataLoader
from flair.models import SequenceTagger
if __name__ == "__main__":
corpus = CONLL_03(base_path="data/conll-2003")
tagger = SequenceTagger.load("models/en-ner-conll03-v0.4.pt")
dev_eval_result, dev_loss = tagger.evaluate(
DataLoader(corpus.test, batch_size=64, num_workers=8))
print(dev_eval_result.main_score)
from flair.data import Corpus
from flair.datasets import CONLL_03
from flair.models import SequenceTagger
# Load the corpus
corpus: Corpus = CONLL_03(base_path='resources/tasks')
tagger: SequenceTagger = SequenceTagger.load('resources/taggers/akbik2018-ner/final-model.pt')
# Uncomment the following to use the author provided tagger (model)
# tagger: SequenceTagger = SequenceTagger.load('ner')
result, _ = tagger.evaluate([corpus.test])
print(result.detailed_results)
parser.add_argument('--patience', default=3, type=int)
parser.add_argument("--use-crf", action="store_true", help="use crf layer")
parser.add_argument("--debug", action="store_true", help="debug")
parser.add_argument(
"--log-file",
default="./code/flair/resources/tasks/conll_03/test_results.csv",
type=str,
help="the file to store resutls")
args = parser.parse_args()
# 1. get the corpus
corpus: Corpus = CONLL_03(base_path='./code/flair/resources/tasks',
tag_to_bioes='ner')
# subsampling the corpus
corpus.train.sentences = corpus.train.sentences[0:args.train_examples]
corpus.train.total_sentence_count = args.train_examples
# knn file
args.knn_idx_file = './code/flair/resources/tasks/conll_03/sent_id_knn{}.pkl'.format(
args.train_examples)
# 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)
from flair.data import Sentence
from flair.data import MultiCorpus
from flair.datasets import CONLL_03
flair.set_seed(1)
#label_name_map = {
#"S-LOC":"Single Location", "B-PER":"Begin Person", "E-PER":"End Person", "S-ORG":"Single Organization", "S-PER":"Single Person", "B-ORG":"Begin Organization", "E-ORG":"End Organization", "I-ORG":"In Organization",
#"S-MISC":"Single Miscellaneous", "B-MISC":"Begin Miscellaneous", "E-MISC":"End Miscellaneous", "I-PER":"In Person", "B-LOC":"Begin Location", "E-LOC":"End Location", "I-MISC":"In Miscellaneous", "I-LOC":"In Location"
#}
label_name_map = {
"LOC":"Location","PER":"Person","ORG":"Organization","MISC":"Miscellaneous"
}
print(label_name_map)
corpus = CONLL_03(label_name_map=label_name_map, base_path="/vol/fob-vol7/mi19/harnisph/studienprojekt-dokumentation")
print(corpus)
corpus = corpus.downsample(0.1)
print(corpus)
tag_type = "ner"
label_dictionary = corpus.make_label_dictionary(tag_type)
print(label_dictionary)
#embeddings = WordEmbeddings("glove")
embeddings = TransformerWordEmbeddings()
tagger = TARSSequenceTagger(tag_dictionary=label_dictionary, tag_type=tag_type, task_name="TEST_NER")
#tagger = SequenceTagger(tag_dictionary=corpus.make_tag_dictionary(tag_type), tag_type=tag_type, hidden_size=256, embeddings=embeddings)
trainer = ModelTrainer(tagger, corpus)
# evaluate using trained model to see if my modification is correct or not
from flair.data import Sentence
from flair.models import SequenceTagger
model = SequenceTagger.load(
'reproduce_ner_10epochs/taggers/sota-ner/final-model.pt')
from flair.data import Corpus
from flair.datasets import CONLL_03
corpus: Corpus = CONLL_03(base_path='reproduce_ner_10epochs/tasks')
testdata = corpus.test
# sentence = Sentence('I love Berlin')
# from tqdm import tqdm
test_result, test_loss = model.evaluate(testdata, out_path='test.tsv')
result_line = f"\t{test_loss}\t{test_result.log_line}"
# import logging
# log = logging.getLogger("test")
# main score is micro averaged f1 score
# result line is precision, recall, micro averaged score
print(f"TEST : loss {test_loss} - score {round(test_result.main_score, 4)}")
print(f"TEST RESULT: {result_line}")
# print(test_result.detailed_results)
# print(result)
# print(sentence.to_tagged_string())
