def test_train_load_use_classifier_flair(results_base_path, tasks_base_path):
corpus = flair.datasets.ClassificationCorpus(tasks_base_path / "imdb")
label_dict = corpus.make_label_dictionary()
flair_document_embeddings: DocumentRNNEmbeddings = DocumentRNNEmbeddings(
[flair_embeddings], 128, 1, False, 64, False, False)
model: TextClassifier = TextClassifier(flair_document_embeddings,
label_dict,
multi_label=False)
trainer = ModelTrainer(model, corpus)
trainer.train(results_base_path, max_epochs=2, shuffle=False)
sentence = Sentence("Berlin is a really nice city.")
for s in model.predict(sentence):
for l in s.labels:
assert l.value is not None
assert 0.0 <= l.score <= 1.0
assert type(l.score) is float
del trainer, model, corpus, flair_document_embeddings
loaded_model = TextClassifier.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)
del loaded_model
def train_model(data_dir, max_epochs):
st.write('Creating word corpus for training...')
corpus = ClassificationCorpus(data_dir)
label_dict = corpus.make_label_dictionary()
st.write('Done')
st.write('Load and create Embeddings for text data...')
word_embeddings = [
WordEmbeddings('glove'),
# FlairEmbeddings('news-forward'),
# FlairEmbeddings('news-backward')
]
document_embeddings = DocumentRNNEmbeddings(word_embeddings,
hidden_size=512,
reproject_words=True,
reproject_words_dimension=256)
st.write('Done')
st.write('Preparing')
classifier = TextClassifier(document_embeddings,
label_dictionary=label_dict)
trainer = ModelTrainer(classifier, corpus)
trainer.train('model-saves',
learning_rate=0.1,
mini_batch_size=32,
anneal_factor=0.5,
patience=8,
max_epochs=max_epochs,
checkpoint=True)
st.write('Model Training Finished!')
def test_train_resume_text_classification_training(results_base_path,
tasks_base_path):
corpus = flair.datasets.ClassificationCorpus(tasks_base_path / "imdb")
label_dict = corpus.make_label_dictionary()
embeddings: TokenEmbeddings = FlairEmbeddings("news-forward-fast")
document_embeddings: DocumentRNNEmbeddings = DocumentRNNEmbeddings(
[embeddings], 128, 1, False)
model = TextClassifier(document_embeddings, label_dict, False)
trainer = ModelTrainer(model, corpus)
trainer.train(results_base_path,
max_epochs=2,
shuffle=False,
checkpoint=True)
trainer = ModelTrainer.load_checkpoint(results_base_path / "checkpoint.pt",
corpus)
trainer.train(results_base_path,
max_epochs=2,
shuffle=False,
checkpoint=True)
# clean up results directory
shutil.rmtree(results_base_path)
def test_train_load_use_classifier_multi_label(results_base_path,
tasks_base_path):
corpus = flair.datasets.ClassificationCorpus(tasks_base_path /
"multi_class")
label_dict = corpus.make_label_dictionary()
word_embedding: WordEmbeddings = WordEmbeddings("turian")
document_embeddings = DocumentRNNEmbeddings(
embeddings=[word_embedding],
hidden_size=32,
reproject_words=False,
bidirectional=False,
)
model: TextClassifier = TextClassifier(document_embeddings,
label_dict,
multi_label=True)
trainer = ModelTrainer(model, corpus)
trainer.train(
results_base_path,
mini_batch_size=1,
max_epochs=100,
shuffle=False,
checkpoint=False,
)
sentence = Sentence("apple tv")
for s in model.predict(sentence):
for l in s.labels:
print(l)
assert l.value is not None
assert 0.0 <= l.score <= 1.0
assert type(l.score) is float
sentence = Sentence("apple tv")
for s in model.predict(sentence):
assert "apple" in sentence.get_label_names()
assert "tv" in sentence.get_label_names()
for l in s.labels:
print(l)
assert l.value is not None
assert 0.0 <= l.score <= 1.0
assert type(l.score) is float
loaded_model = TextClassifier.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_train_classifier_with_sampler(results_base_path, tasks_base_path):
corpus = flair.datasets.ClassificationCorpus(tasks_base_path / "imdb")
label_dict = corpus.make_label_dictionary()
word_embedding: WordEmbeddings = WordEmbeddings("turian")
document_embeddings: DocumentRNNEmbeddings = DocumentRNNEmbeddings(
[word_embedding], 32, 1, False, 64, False, False)
model: TextClassifier = TextClassifier(document_embeddings, label_dict,
False)
trainer = ModelTrainer(model, corpus)
trainer.train(
results_base_path,
max_epochs=2,
shuffle=False,
sampler=ImbalancedClassificationDatasetSampler,
)
sentence = Sentence("Berlin is a really nice city.")
for s in model.predict(sentence):
for l in s.labels:
assert l.value is not None
assert 0.0 <= l.score <= 1.0
assert type(l.score) is float
loaded_model = TextClassifier.load(results_base_path / "final-model.pt")
# clean up results directory
shutil.rmtree(results_base_path)
def test_train_load_use_classifier_multi_label(results_base_path,
tasks_base_path):
corpus = flair.datasets.ClassificationCorpus(tasks_base_path /
"multi_class",
label_type="topic")
label_dict = corpus.make_label_dictionary(label_type="topic")
model: TextClassifier = TextClassifier(
document_embeddings=document_embeddings,
label_dictionary=label_dict,
label_type="topic",
multi_label=True)
trainer = ModelTrainer(model, corpus)
trainer.train(
results_base_path,
mini_batch_size=1,
max_epochs=100,
shuffle=False,
checkpoint=False,
train_with_test=True,
train_with_dev=True,
)
sentence = Sentence("apple tv")
model.predict(sentence)
for label in sentence.labels:
print(label)
assert label.value is not None
assert 0.0 <= label.score <= 1.0
assert type(label.score) is float
sentence = Sentence("apple tv")
model.predict(sentence)
assert "apple" in sentence.get_label_names()
assert "tv" in sentence.get_label_names()
for label in sentence.labels:
assert label.value is not None
assert 0.0 <= label.score <= 1.0
assert type(label.score) is float
del trainer, model, corpus
loaded_model = TextClassifier.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)
del loaded_model
def test_train_load_use_classifier_multi_label(results_base_path,
tasks_base_path):
# corpus = NLPTaskDataFetcher.load_corpus('multi_class', base_path=tasks_base_path)
corpus = NLPTaskDataFetcher.load_classification_corpus(
data_folder=tasks_base_path / 'multi_class')
label_dict = corpus.make_label_dictionary()
glove_embedding: WordEmbeddings = WordEmbeddings('en-glove')
document_embeddings = DocumentLSTMEmbeddings(embeddings=[glove_embedding],
hidden_size=32,
reproject_words=False,
bidirectional=False)
model = TextClassifier(document_embeddings, label_dict, multi_label=True)
trainer = ModelTrainer(model, corpus)
trainer.train(results_base_path,
EvaluationMetric.MICRO_F1_SCORE,
max_epochs=100,
test_mode=True,
checkpoint=False)
sentence = Sentence('apple tv')
for s in model.predict(sentence):
for l in s.labels:
print(l)
assert (l.value is not None)
assert (0.0 <= l.score <= 1.0)
assert (type(l.score) is float)
sentence = Sentence("apple tv")
for s in model.predict(sentence):
assert ('apple' in sentence.get_label_names())
assert ('tv' in sentence.get_label_names())
for l in s.labels:
print(l)
assert (l.value is not None)
assert (0.0 <= l.score <= 1.0)
assert (type(l.score) is float)
loaded_model = TextClassifier.load_from_file(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 main(args, model_dir):
logger.info('Args = {}'.format(args))
corpus = CorpusLoader().load_corpus(CORPUS_PATH[args.corpus])
tokenizer = TokenizerFactory().tokenizer(args.corpus)
logger.info('Loaded corpus: {}'.format(corpus))
logger.info('Get sentences...')
train_sents, _ = flair_utils.standoff_to_flair_sents(corpus.train, tokenizer, verbose=True)
dev_sents, _ = flair_utils.standoff_to_flair_sents(corpus.dev, tokenizer, verbose=True)
test_sents, test_docs = flair_utils.standoff_to_flair_sents(corpus.test,
tokenizer, verbose=True)
train_sents = train_sents + dev_sents
train_sents_filtered = list(filter(lambda sent: not _ignore_sentence(sent), train_sents))
sample_size = int(len(train_sents_filtered) * args.train_sample_frac)
rs = RandomState(seed=args.random_seed)
train_sents_sample = rs.choice(train_sents_filtered, replace=False, size=sample_size).tolist()
logger.info('Train with fraction of training data: {} sents out of {} sentences ({}%)',
sample_size, len(train_sents_filtered), args.train_sample_frac)
# We need to pass some dev data, otherwise flair raises a ZeroDivisionError
# See: https://github.com/zalandoresearch/flair/issues/1139
# We just split the training sample into half and instruct Flair to train_with_dev (see below).
half = len(train_sents_sample) // 2
flair_corpus = flair_utils.FilteredCorpus(train=train_sents_sample[:half],
dev=train_sents_sample[half:],
test=test_sents,
ignore_sentence=_ignore_sentence)
logger.info(flair_corpus)
logger.info('Train model...')
tagger = run_bilstmcrf.get_model(flair_corpus,
corpus_name=args.corpus,
embedding_lang=args.embedding_lang,
pooled_contextual_embeddings=True)
trainer = ModelTrainer(tagger, flair_corpus)
trainer.train(join(model_dir, 'flair'),
max_epochs=150,
monitor_train=False,
train_with_dev=True,
save_final_model=args.save_final_model)
logger.info('Make predictions...')
run_bilstmcrf.make_predictions(tagger, flair_corpus)
logger.info('Start evaluation...')
evaluator = Evaluator(gold=corpus.test,
predicted=flair_utils.flair_sents_to_standoff(test_sents, test_docs))
entity_level_metric = evaluator.entity_level()
logger.info('\n{}', entity_level_metric)
entity_level_metric.to_csv(join(model_dir, 'scores_entity.csv'))
evaluator.token_level().to_csv(join(model_dir, 'scores_token.csv'))
evaluator.token_level_blind().to_csv(join(model_dir, 'scores_token_blind.csv'))
logger.info('Done.')
def test_text_classifier_multi(results_base_path, tasks_base_path):
flair.set_seed(123)
corpus = ClassificationCorpus(
tasks_base_path / "trivial" / "trivial_text_classification_multi",
label_type="city",
)
label_dict = corpus.make_label_dictionary(label_type="city")
model: TextClassifier = TextClassifier(
document_embeddings=DocumentPoolEmbeddings([turian_embeddings],
fine_tune_mode="linear"),
label_dictionary=label_dict,
label_type="city",
multi_label=True,
)
trainer = ModelTrainer(model, corpus)
trainer.train(results_base_path,
mini_batch_size=2,
max_epochs=50,
shuffle=True)
# check if model can predict
sentence = Sentence("this is Berlin")
sentence_empty = Sentence(" ")
model.predict(sentence)
model.predict([sentence, sentence_empty])
model.predict([sentence_empty])
# load model
loaded_model = TextClassifier.load(results_base_path / "final-model.pt")
# chcek if model predicts correct label
sentence = Sentence("this is Berlin")
sentence_double = Sentence("this is Berlin and pizza")
loaded_model.predict([sentence, sentence_double])
values = []
for label in sentence_double.labels:
assert label.value is not None
assert 0.0 <= label.score <= 1.0
assert type(label.score) is float
values.append(label.value)
assert "Berlin" in values
assert "pizza" in values
# check if loaded model successfully fit the training data
result: Result = loaded_model.evaluate(corpus.test, gold_label_type="city")
print(result.classification_report)
assert result.classification_report["micro avg"]["f1-score"] == 1.0
del loaded_model
def trainFlairClassifier(df, columns, trainNameCsv, testNameCsv, devNameCsv,
classifierFileName):
ids = df['id'].tolist()
nSamples = len(ids)
idx80 = int(nSamples * 0.7)
idx90 = int(nSamples * 0.9)
train_ids = ids[:idx80]
test_ids = ids[idx80:idx90]
dev_ids = ids[idx90:]
with TemporaryDirectory() as temp_dir:
trainCsv = temp_dir + trainNameCsv
testCsv = temp_dir + testNameCsv
devCsv = temp_dir + devNameCsv
df[df['id'].isin(train_ids)].to_csv(trainCsv,
columns=columns,
sep='\t',
index=False,
header=False)
df[df['id'].isin(test_ids)].to_csv(testCsv,
columns=columns,
sep='\t',
index=False,
header=False)
df[df['id'].isin(dev_ids)].to_csv(devCsv,
columns=columns,
sep='\t',
index=False,
header=False)
corpus = NLPTaskDataFetcher.load_classification_corpus(
temp_dir, train_file=trainCsv, test_file=testCsv, dev_file=devCsv)
word_embeddings = [
WordEmbeddings('glove'),
FlairEmbeddings('news-forward-fast'),
FlairEmbeddings('news-backward-fast')
]
document_embeddings = DocumentLSTMEmbeddings(
word_embeddings,
hidden_size=512,
reproject_words=True,
reproject_words_dimension=256)
classifier = TextClassifier(
document_embeddings,
label_dictionary=corpus.make_label_dictionary(),
multi_label=False)
trainer = ModelTrainer(classifier, corpus)
trainer.train(temp_dir, max_epochs=50)
classifier.save(classifierFileName)
def run_zero_shot(train_tweets, train_y, val_tweets, val_y):
"""
Performs the training of the zero shot learning model
@param train_tweets: the tweets that will be used for training
@param train_y: the training labels
@param val_tweets: the tweets that will be used for validation
@param val_y: the validation labels
@return: None
"""
# 1. Load our pre-trained TARS model for English
print("Zero shot")
# download https://nlp.informatik.hu-berlin.de/resources/models/tars-base/tars-base.pt
tars = TARSClassifier.load(
os.path.join(os.path.dirname(__file__), "..", "..", "saved_models",
"tars-base.pt"))
train_tweets["output"] = train_y.iloc[:]
train = train_tweets.apply(create_sentences, axis=1).tolist()
train = SentenceDataset(train)
val_tweets["output"] = val_y.iloc[:]
val = val_tweets.apply(create_sentences, axis=1).tolist()
val = SentenceDataset(val)
corpus = Corpus(train=train, test=val)
tars.add_and_switch_to_new_task(
"POSITIVE_NEGATIVE", label_dictionary=corpus.make_label_dictionary())
trainer = ModelTrainer(tars, corpus)
# 4. train model
trainer.train(
base_path='../../data/zero_shot', # path to store the model artifacts
learning_rate=0.02, # use very small learning rate
mini_batch_size=16, # small mini-batch size since corpus is tiny
max_epochs=10, # terminate after 10 epochs
)
print("DONE TRAINING")
tars = TARSClassifier.load('../../model/zero_shot/final-model.pt')
val_tweets["pred"] = val_tweets.apply(predict_few_shot,
args=(tars, ),
axis=1)
val_tweets["pred"] = val_tweets["pred"].apply(lambda x: 1
if x == "positive" else -1)
pred = pd.DataFrame(list(val_tweets["pred"]), columns=['Prediction'])
pred.index += 1
pred.insert(0, 'Id', pred.index)
pred.to_csv("../../predictions/zero_shot_pred.csv", index=False)
def main(args):
args = parser.parse_args()
# 1. get the corpus
corpus: TaggedCorpus = NLPTaskDataFetcher.load_classification_corpus(
args.data_dir[0],
train_file='train.txt',
dev_file='dev.txt',
test_file='test.txt')
# 2. create the label dictionary
label_dict = corpus.make_label_dictionary()
# 3. make a list of word embeddings
word_embeddings = [
WordEmbeddings('glove'),
# comment in flair embeddings for state-of-the-art results
# FlairEmbeddings('news-forward'),
# FlairEmbeddings('news-backward'),
# ELMoEmbeddings()
]
# 4. init document embedding by passing list of word embeddings
document_embeddings: DocumentLSTMEmbeddings = DocumentLSTMEmbeddings(
word_embeddings,
hidden_size=128,
reproject_words=True,
reproject_words_dimension=64,
)
# 5. create the text classifier
classifier = TextClassifier(document_embeddings,
label_dictionary=label_dict,
multi_label=False)
# 6. initialize the text classifier trainer
trainer = ModelTrainer(classifier, corpus)
# 7. start the training
model_out = 'resources/classifiers/sentence-classification/glove'
trainer.train(model_out,
learning_rate=0.1,
mini_batch_size=32,
anneal_factor=0.5,
patience=5,
max_epochs=100)
# 8. plot training curves (optional)
from flair.visual.training_curves import Plotter
plotter = Plotter()
plotter.plot_training_curves(join(model_out, 'loss.tsv'))
plotter.plot_weights(join(model_out, 'weights.txt'))
def train(self):
corpus = NLPTaskDataFetcher.load_classification_corpus(Path(self.corpus_path),
test_file="test_clean_text.txt",
dev_file="dev_clean_text.txt",
train_file="train_clean_text.txt")
embeddings = [WordEmbeddings(self.word_emb_path), FlairEmbeddings('polish-forward'),
FlairEmbeddings('polish-backward')]
document_embeddings = DocumentRNNEmbeddings(embeddings, hidden_size=self.hidden_size, bidirectional=True)
classifier = TextClassifier(document_embeddings, label_dictionary=corpus.make_label_dictionary(),
multi_label=False)
trainer = ModelTrainer(classifier, corpus)
trainer.train(self.model_path, evaluation_metric=EvaluationMetric.MACRO_F1_SCORE, max_epochs=self.epochs)
def fit(self, X, y):
""" Build feature vectors and train FLAIR model.
Parameters
----------
X : list(list(str))
list of sentences. Sentences are tokenized into list
of words.
y : list(list(str))
list of list of BIO tags.
Returns
-------
self
"""
log.info("Creating FLAIR corpus...")
Xtrain, Xval, ytrain, yval = train_test_split(X, y, test_size=0.1)
sents_train = self._convert_to_flair(Xtrain, ytrain)
sents_val = self._convert_to_flair(Xval, yval)
corpus_train = Corpus(sents_train, sents_val, [], name="train-corpus")
tag_dict = corpus_train.make_tag_dictionary(tag_type="ner")
if self.embeddings is None:
embedding_types = [
WordEmbeddings("glove"),
CharacterEmbeddings()
]
self.embeddings = StackedEmbeddings(embeddings=embedding_types)
log.info("Building FLAIR NER...")
self.model_ = SequenceTagger(hidden_size=self.hidden_dim,
embeddings=self.embeddings,
tag_dictionary=tag_dict,
tag_type="ner",
use_crf=self.use_crf,
use_rnn=self.use_rnn,
rnn_layers=self.num_rnn_layers,
dropout=self.dropout,
word_dropout=self.word_dropout,
locked_dropout=self.locked_dropout)
log.info("Training FLAIR NER...")
opt = torch.optim.SGD if self.optimizer == "sgd" else torch.optim.Adam
trainer = ModelTrainer(self.model_, corpus_train, opt)
trainer.train(base_path=self.basedir,
learning_rate=self.learning_rate,
mini_batch_size=self.batch_size,
max_epochs=self.max_iter)
return self
def train(self):
from flair.data import Corpus
from flair.datasets import SentenceDataset
from flair.data import Sentence
self.classes = utils.read_class_titles(settings.CAT_DEPTH)
self.classes['NOCAT'] = 'NOCAT'
train = SentenceDataset([
Sentence(row['titlen']).add_label('law_topic',
self.classes[row['cat1']])
for i, row in self.df_train.iterrows()
])
# make a corpus with train and test split
self.corpus = Corpus(train=train, dev=train)
# 1. load base TARS
tars = self._load_pretained_model()
# 2. make the model aware of the desired set of labels from the new corpus
tars.add_and_switch_to_new_task(
"LAW_TOPIC", label_dictionary=self.corpus.make_label_dictionary())
# 3. initialize the text classifier trainer with your corpus
from flair.trainers import ModelTrainer
trainer = ModelTrainer(tars, self.corpus)
# 4. train model
path = settings.WORKING_DIR
if 1:
trainer.train(
base_path=path,
# path to store the model artifacts
learning_rate=5e-2, # 5ep, 0.2 bad; 5ep with 0.1 looks ok.
mini_batch_size=settings.MINIBATCH,
# mini_batch_chunk_size=1, mini_batch_chunk_size=4, # optionally set this if transformer is too much for your machine
max_epochs=settings.EPOCHS, # terminate after 10 epochs
train_with_dev=False,
save_final_model=False,
param_selection_mode=True, # True to avoid model saves
shuffle=False, # Already done
)
# from flair.models.text_classification_model import TARSClassifier
# self.model = TARSClassifier.load(
# os.path.join(path, 'best-model.pt')
# )
self.model = tars
def classify(data, labels, test, train, validation):
train_data = [k for k in data.keys() if k in train]
train_labels = [labels[k] for k in train_data]
train_data = [data[k] for k in train_data]
test_data = [k for k in data.keys() if k in test]
test_labels = [labels[k] for k in test_data]
test_data = [data[k] for k in test_data]
validation_data = [k for k in data.keys() if k in validation]
validation_labels = [labels[k] for k in validation_data]
validation_data = [data[k] for k in validation_data]
save_training_files(train_data, train_labels, test_data, test_labels,
validation_data, validation_labels)
corpus = NLPTaskDataFetcher.load_classification_corpus(
Path('./'),
test_file='test.txt',
dev_file='dev.txt',
train_file='train.txt')
word_embeddings = [
WordEmbeddings('pl'),
FlairEmbeddings('polish-forward'),
FlairEmbeddings('polish-backward')
]
doc_embeddings = DocumentRNNEmbeddings(word_embeddings,
hidden_size=512,
reproject_words=True,
reproject_words_dimension=256)
classifier = TextClassifier(
doc_embeddings,
label_dictionary=corpus.make_label_dictionary(),
multi_label=False)
trainer = ModelTrainer(classifier, corpus)
trainer.train('./', max_epochs=25)
classifier = TextClassifier.load_from_file('./best-model.pt')
validation_data = [Sentence(x) for x in validation_data]
for x in validation_data:
classifier.predict(x)
predicted = [int(x.labels[0].value) for x in validation_data]
remove_training_files()
precision, recall, f1, _ = precision_recall_fscore_support(
validation_labels, predicted, average='binary')
return {
'accuracy': float("{:.3f}".format(round(precision, 3))),
'recall': float("{:.3f}".format(round(recall, 3))),
'f1': float("{:.3f}".format(round(f1, 3)))
}
def fit(self, corpus: Corpus, model_path: str):
self.model = TARSClassifier(
task_name="ChemicalUnderstanding",
label_dictionary=corpus.make_label_dictionary(),
)
trainer = ModelTrainer(self.model, corpus)
trainer.train(
base_path=model_path,
learning_rate=0.02,
mini_batch_size=16,
mini_batch_chunk_size=4,
max_epochs=10,
)
def train_model(self,
corpus,
classifier,
step_num,
optimizer_state=None,
epoch=1,
lr=1e-3):
trainer = ModelTrainer(classifier,
corpus,
optimizer=AdamW,
optimizer_state=optimizer_state)
result = trainer.train(
f'{self.experiment_name}/{step_num}/',
learning_rate=lr,
min_learning_rate=1e-8,
mini_batch_size=32,
anneal_factor=0.5,
patience=5,
max_epochs=epoch,
embeddings_storage_mode=self.embeddings_storage_mode,
weight_decay=1e-4,
)
os.system(f'rm {self.experiment_name}/{step_num-3}/best-model.pt')
os.system(f'rm {self.experiment_name}/{step_num-3}/final-model.pt')
return classifier, result['optimizer_state_dict']
def train(self):
tox_corpus = FlairTox21().to_corpus()
self.model = TARSClassifier(
task_name="Toxicity",
label_dictionary=tox_corpus.make_label_dictionary(),
document_embeddings="distilbert-base-uncased",
)
trainer = ModelTrainer(self.model, tox_corpus)
trainer.train(
base_path=get_path("model") / self.filename,
learning_rate=0.02,
mini_batch_size=1,
max_epochs=10,
)
def test_train_resume_text_classification_training(results_base_path, tasks_base_path):
corpus = NLPTaskDataFetcher.load_corpus(NLPTask.IMDB, base_path=tasks_base_path)
label_dict = corpus.make_label_dictionary()
embeddings: TokenEmbeddings = FlairEmbeddings('news-forward-fast', use_cache=False)
document_embeddings: DocumentLSTMEmbeddings = DocumentLSTMEmbeddings([embeddings], 128, 1, False)
model = TextClassifier(document_embeddings, label_dict, False)
trainer = ModelTrainer(model, corpus)
trainer.train(results_base_path, max_epochs=2, test_mode=True, checkpoint=True)
trainer = ModelTrainer.load_from_checkpoint(results_base_path / 'checkpoint.pt', 'TextClassifier', corpus)
trainer.train(results_base_path, max_epochs=2, test_mode=True, checkpoint=True)
# clean up results directory
shutil.rmtree(results_base_path)
def train(self, train_data):
flair_logger = logging.getLogger("flair")
handler = RequestsHandler()
flair_logger.addHandler(handler)
filter = ListenFilter("filter", self.args)
flair_logger.addFilter(filter)
trainer = ModelTrainer(self.model, self.corpus)
trainer.train(self.model_base_path(),
learning_rate=0.1,
mini_batch_size=32,
max_epochs=self.args.iter if self.args.max_iter else 100,
train_with_dev=True,
monitor_test=True,
embeddings_storage_mode="gpu")
def train_ner(device_category):
"""
Training the sequence labeling model
"""
columns = {0: 'text', 1: 'ner'}
training_file = os.path.join(
root_path, 'part_extraction/data/{}.conll'.format(device_category))
data_folder = os.path.join(root_path, 'part_extraction/data')
corpus = ColumnCorpus(data_folder, columns, train_file=training_file)
print(len(corpus.train))
tag_type = 'ner'
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
print(tag_dictionary.idx2item)
embedding_types = [
WordEmbeddings('glove'),
# comment in this line to use character embeddings
# CharacterEmbeddings(),
FlairEmbeddings('news-forward'),
FlairEmbeddings('news-backward'),
]
embeddings = StackedEmbeddings(embeddings=embedding_types)
tagger = SequenceTagger(hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type=tag_type,
use_crf=True)
trainer = ModelTrainer(tagger, corpus)
# 7. start training
trainer.train(ner_models,
learning_rate=0.1,
mini_batch_size=32,
max_epochs=150)
trainer.model.save('{}/{}.pt'.format(ner_models, device_category))
def handle(self, *args, **options):
file = options.get('file') or 'annotated_sentences'
model_folder = options.get('model_folder') or 'model-var'
columns = {0: 'text', 1: 'var'}
data_folder = 'data/txt'
corpus = ColumnCorpus(data_folder, columns,
train_file=f'{file}.txt')
tag_type = 'var'
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
embedding_types = [
WordEmbeddings('glove'),
# comment in this line to use character embeddings
# CharacterEmbeddings(),
# comment in these lines to use flair embeddings
# FlairEmbeddings('news-forward'),
# FlairEmbeddings('news-backward'),
TransformerWordEmbeddings('bert-base-uncased'),
]
embeddings = StackedEmbeddings(embeddings=embedding_types)
tagger = SequenceTagger(hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type=tag_type,
use_crf=True)
trainer = ModelTrainer(tagger, corpus)
trainer.train(f'data/models/taggers/{model_folder}',
learning_rate=0.1,
mini_batch_size=32,
max_epochs=150)
self.stdout.write(self.style.SUCCESS(f'Successfully trained model on dataset file.'))
def start(self) -> None:
self.stacked_embeddings = self._get_stacked_embeddings()
description = self.experiment.description.replace(" ", "_")
batch_size = self.experiment.batch_size
max_epochs = self.experiment.max_epochs
embeddings_storage_mode = self.experiment.embeddings_storage_mode
train_with_dev = self.experiment.train_with_dev
tagger, corpus = self._get_sequence_tagger()
trainer = ModelTrainer(tagger, corpus)
trainer.train(
f"resources/taggers/experiment_{description}_{self.number}",
learning_rate=0.1,
mini_batch_size=batch_size,
max_epochs=max_epochs,
embeddings_storage_mode=embeddings_storage_mode,
train_with_dev=train_with_dev,
)
def test_train_resume_sequence_tagging_training(results_base_path, tasks_base_path):
corpus = NLPTaskDataFetcher.load_corpora([NLPTask.FASHION, NLPTask.GERMEVAL], base_path=tasks_base_path)
tag_dictionary = corpus.make_tag_dictionary('ner')
embeddings = WordEmbeddings('glove')
model: SequenceTagger = SequenceTagger(hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type='ner',
use_crf=False)
trainer = ModelTrainer(model, corpus)
trainer.train(results_base_path, max_epochs=2, test_mode=True, checkpoint=True)
trainer = ModelTrainer.load_from_checkpoint(results_base_path / 'checkpoint.pt', 'SequenceTagger', corpus)
trainer.train(results_base_path, max_epochs=2, test_mode=True, checkpoint=True)
# clean up results directory
shutil.rmtree(results_base_path)
def test_train_classifier_with_sampler(results_base_path, tasks_base_path):
corpus = flair.datasets.ClassificationCorpus((tasks_base_path / 'imdb'))
label_dict = corpus.make_label_dictionary()
word_embedding = WordEmbeddings('turian')
document_embeddings = DocumentRNNEmbeddings([word_embedding], 32, 1, False,
64, False, False)
model = TextClassifier(document_embeddings, label_dict, False)
trainer = ModelTrainer(model, corpus)
trainer.train(results_base_path,
max_epochs=2,
shuffle=False,
sampler=ImbalancedClassificationDatasetSampler)
sentence = Sentence('Berlin is a really nice city.')
for s in model.predict(sentence):
for l in s.labels:
assert (l.value is not None)
assert (0.0 <= l.score <= 1.0)
assert (type(l.score) is float)
loaded_model = TextClassifier.load((results_base_path / 'final-model.pt'))
shutil.rmtree(results_base_path)
def test_train_resume_text_classification_training(results_base_path,
tasks_base_path):
corpus = flair.datasets.ClassificationCorpus((tasks_base_path / 'imdb'))
label_dict = corpus.make_label_dictionary()
embeddings = FlairEmbeddings('news-forward-fast')
document_embeddings = DocumentRNNEmbeddings([embeddings], 128, 1, False)
model = TextClassifier(document_embeddings, label_dict, False)
trainer = ModelTrainer(model, corpus)
trainer.train(results_base_path,
max_epochs=2,
shuffle=False,
checkpoint=True)
checkpoint = TextClassifier.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 fine_tune(self):
if isinstance(self.document_embedding, TransformerDocumentEmbeddings):
corpus = TREC_6()
label_dict = corpus.make_label_dictionary()
classifier = TextClassifier(self.document_embedding,
label_dictionary=label_dict)
trainer = ModelTrainer(classifier, corpus, optimizer=Adam)
# 6. start the training
trainer.train(
'resources/taggers/trec',
learning_rate=3e-5, # use very small learning rate
mini_batch_size=16,
mini_batch_chunk_size=
4, # optionally set this if transformer is too much for your machine
max_epochs=5, # terminate after 5 epochs
)
else:
raise UserWarning(
"No fine tuning for this embedding type implemented")
def train_sentiment_model(rootdir, train, dev, test, num_epochs, device, outputdir):
flair.device = torch.device(device)
corpus = ClassificationCorpus(rootdir,
train_file=train,
dev_file=dev,
test_file=test,
in_memory=False)
label_dict = corpus.make_label_dictionary()
# init Flair embeddings
flair_forward_embedding = FlairEmbeddings('multi-forward')
flair_backward_embedding = FlairEmbeddings('multi-backward')
optional_embedding = ELMoEmbeddings('original')
word_embeddings = list(filter(None, [
optional_embedding,
FlairEmbeddings('news-forward'),
FlairEmbeddings('news-backward'),
]))
# Initialize document embedding by passing list of word embeddings
#
# Note this will kick off model generation that will take a long time (several hours)
# This will produce final-model.pt and best-model.pt files which represent a stored trained model.
document_embeddings = DocumentRNNEmbeddings(
word_embeddings,
hidden_size=512,
reproject_words=True,
reproject_words_dimension=256,
)
classifier = TextClassifier(document_embeddings,
label_dictionary=label_dict,
multi_label=False)
trainer = ModelTrainer(classifier, corpus)
trainer.train(outputdir, max_epochs=num_epochs)
def test_train_load_use_classifier_with_sampler(results_base_path,
tasks_base_path):
corpus = flair.datasets.ClassificationCorpus(tasks_base_path / "imdb",
label_type="topic")
label_dict = corpus.make_label_dictionary(label_type="topic")
model: TextClassifier = TextClassifier(
document_embeddings=document_embeddings,
label_dictionary=label_dict,
label_type="topic",
multi_label=False)
trainer = ModelTrainer(model, corpus)
trainer.train(
results_base_path,
max_epochs=2,
shuffle=False,
sampler=ImbalancedClassificationDatasetSampler,
)
sentence = Sentence("Berlin is a really nice city.")
model.predict(sentence)
for label in sentence.labels:
assert label.value is not None
assert 0.0 <= label.score <= 1.0
assert type(label.score) is float
del trainer, model, corpus
loaded_model = TextClassifier.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)
del loaded_model
