def test_train_resume_classifier(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(
document_embeddings=document_embeddings,
label_dictionary=label_dict,
multi_label=False,
label_type="topic",
)
# 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 = TextClassifier.load(results_base_path / "checkpoint.pt")
with pytest.warns(UserWarning):
trainer.resume(model=checkpoint_model, max_epochs=4)
del trainer
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
