def test_doc_classification(caplog):
caplog.set_level(logging.CRITICAL)
set_all_seeds(seed=42)
device, n_gpu = initialize_device_settings(use_cuda=False)
n_epochs = 1
batch_size = 8
evaluate_every = 30
lang_model = "bert-base-german-cased"
tokenizer = BertTokenizer.from_pretrained(
pretrained_model_name_or_path=lang_model, do_lower_case=False)
processor = GermEval18CoarseProcessor(tokenizer=tokenizer,
max_seq_len=64,
data_dir="samples/doc_class",
train_filename="train-sample.tsv",
test_filename=None)
data_silo = DataSilo(processor=processor, batch_size=batch_size)
language_model = Bert.load(lang_model)
prediction_head = TextClassificationHead(
layer_dims=[768, len(processor.label_list)])
model = AdaptiveModel(language_model=language_model,
prediction_heads=[prediction_head],
embeds_dropout_prob=0.1,
lm_output_types=["per_sequence"],
device=device)
optimizer, warmup_linear = initialize_optimizer(
model=model,
learning_rate=2e-5,
warmup_proportion=0.1,
n_examples=data_silo.n_samples("train"),
batch_size=batch_size,
n_epochs=1)
trainer = Trainer(optimizer=optimizer,
data_silo=data_silo,
epochs=n_epochs,
n_gpu=n_gpu,
warmup_linear=warmup_linear,
evaluate_every=evaluate_every,
device=device)
model = trainer.train(model)
save_dir = "testsave/doc_class"
model.save(save_dir)
processor.save(save_dir)
basic_texts = [
{
"text":
"Schartau sagte dem Tagesspiegel, dass Fischer ein Idiot sei"
},
{
"text": "Martin Müller spielt Handball in Berlin"
},
]
model = Inferencer.load(save_dir)
result = model.run_inference(dicts=basic_texts)
assert result[0]["predictions"][0]["label"] == "OTHER"
assert abs(result[0]["predictions"][0]["probability"] -
0.5358161) <= 0.0001
trainer = Trainer(
optimizer=optimizer,
data_silo=data_silo,
epochs=n_epochs,
n_gpu=n_gpu,
warmup_linear=warmup_linear,
evaluate_every=evaluate_every,
device=device,
)
# 7. Let it grow
model = trainer.train(model)
# 8. Hooray! You have a model. Store it:
save_dir = "saved_models/bert-german-ner-tutorial"
model.save(save_dir)
processor.save(save_dir)
# 9. Load it & harvest your fruits (Inference)
basic_texts = [
{
"text": "Schartau sagte dem Tagesspiegel, dass Fischer ein Idiot sei"
},
{
"text": "Martin Müller spielt Handball in Berlin"
},
]
model = Inferencer.load(save_dir)
result = model.run_inference(dicts=basic_texts)
print(result)
def test_qa(caplog):
caplog.set_level(logging.CRITICAL)
set_all_seeds(seed=42)
device, n_gpu = initialize_device_settings(use_cuda=False)
batch_size = 2
n_epochs = 1
evaluate_every = 4
base_LM_model = "bert-base-cased"
tokenizer = BertTokenizer.from_pretrained(
pretrained_model_name_or_path=base_LM_model, do_lower_case=False)
label_list = ["start_token", "end_token"]
processor = SquadProcessor(tokenizer=tokenizer,
max_seq_len=16,
max_query_length=4,
train_filename="train-sample.json",
dev_filename="dev-sample.json",
test_filename=None,
data_dir="samples/qa",
labels=label_list,
metric="squad")
data_silo = DataSilo(processor=processor, batch_size=batch_size)
language_model = Bert.load(base_LM_model)
prediction_head = QuestionAnsweringHead(layer_dims=[768, len(label_list)])
model = AdaptiveModel(
language_model=language_model,
prediction_heads=[prediction_head],
embeds_dropout_prob=0.1,
lm_output_types=["per_token"],
device=device,
)
optimizer, warmup_linear = initialize_optimizer(
model=model,
learning_rate=1e-5,
warmup_proportion=0.2,
n_batches=len(data_silo.loaders["train"]),
n_epochs=n_epochs,
)
trainer = Trainer(
optimizer=optimizer,
data_silo=data_silo,
epochs=n_epochs,
n_gpu=n_gpu,
warmup_linear=warmup_linear,
evaluate_every=evaluate_every,
device=device,
)
model = trainer.train(model)
save_dir = "testsave/qa"
model.save(save_dir)
processor.save(save_dir)
QA_input = [{
"questions": ["In what country is Normandy located?"],
"text":
'The Normans (Norman: Nourmands; French: Normands; Latin: Normanni) were the people who in the 10th and 11th centuries gave their name to Normandy, a region in France. They were descended from Norse ("Norman" comes from "Norseman") raiders and pirates from Denmark, Iceland and Norway who, under their leader Rollo, agreed to swear fealty to King Charles III of West Francia. Through generations of assimilation and mixing with the native Frankish and Roman-Gaulish populations, their descendants would gradually merge with the Carolingian-based cultures of West Francia. The distinct cultural and ethnic identity of the Normans emerged initially in the first half of the 10th century, and it continued to evolve over the succeeding centuries.',
}]
model = Inferencer.load(save_dir)
result = model.run_inference(dicts=QA_input)
assert isinstance(result[0]["predictions"][0]["end"], int)
def test_doc_regression(caplog):
caplog.set_level(logging.CRITICAL)
set_all_seeds(seed=42)
device, n_gpu = initialize_device_settings(use_cuda=False)
n_epochs = 1
batch_size = 8
evaluate_every = 30
lang_model = "bert-base-cased"
tokenizer = BertTokenizer.from_pretrained(
pretrained_model_name_or_path=lang_model, do_lower_case=False)
processor = RegressionProcessor(tokenizer=tokenizer,
max_seq_len=128,
data_dir="samples/doc_regr",
columns=["text", "label"],
label_list=[],
metrics=["mse"],
train_filename="train-sample.tsv",
test_filename=None)
data_silo = DataSilo(processor=processor, batch_size=batch_size)
language_model = Bert.load(lang_model)
prediction_head = RegressionHead(layer_dims=[768, 1])
model = AdaptiveModel(language_model=language_model,
prediction_heads=[prediction_head],
embeds_dropout_prob=0.1,
lm_output_types=["per_sequence_continuous"],
device=device)
optimizer, warmup_linear = initialize_optimizer(
model=model,
learning_rate=2e-5,
warmup_proportion=0.1,
n_batches=len(data_silo.loaders["train"]),
n_epochs=1)
trainer = Trainer(optimizer=optimizer,
data_silo=data_silo,
epochs=n_epochs,
n_gpu=n_gpu,
warmup_linear=warmup_linear,
evaluate_every=evaluate_every,
device=device)
model = trainer.train(model)
save_dir = "testsave/doc_regr"
model.save(save_dir)
processor.save(save_dir)
basic_texts = [
{
"text":
"The dress is just fabulous and it totally fits my size. The fabric is of great quality and the seams are really well hidden. I am super happy with this purchase and I am looking forward to trying some more from the same brand."
},
{
"text":
"it just did not fit right. The top is very thin showing everything."
},
]
model = Inferencer.load(save_dir)
result = model.run_inference(dicts=basic_texts)
print(result)
assert abs(float(result[0]["predictions"][0]["pred"]) -
4.2121115) <= 0.0001
assert abs(float(result[0]["predictions"][1]["pred"]) -
4.1987348) <= 0.0001
# 6. Feed everything to the Trainer, which keeps care of growing our model and evaluates it from time to time
trainer = Trainer(
optimizer=optimizer,
data_silo=data_silo,
epochs=n_epochs,
n_gpu=n_gpu,
warmup_linear=warmup_linear,
evaluate_every=evaluate_every,
device=device,
)
# 7. Let it grow! Watch the tracked metrics live on the public mlflow server: http://80.158.39.167:5000/
model = trainer.train(model)
# 8. Hooray! You have a model. Store it:
save_dir = "../saved_models/bert-english-qa-tutorial"
model.save(save_dir)
processor.save(save_dir)
# 9. Load it & harvest your fruits (Inference)
QA_input = [{
"questions": ["Who counted the game among the best ever made?"],
"text":
"Twilight Princess was released to universal critical acclaim and commercial success. It received perfect scores from major publications such as 1UP.com, Computer and Video Games, Electronic Gaming Monthly, Game Informer, GamesRadar, and GameSpy. On the review aggregators GameRankings and Metacritic, Twilight Princess has average scores of 95% and 95 for the Wii version and scores of 95% and 96 for the GameCube version. GameTrailers in their review called it one of the greatest games ever created."
}]
model = Inferencer.load(save_dir)
result = model.run_inference(dicts=QA_input)
for x in result:
pprint.pprint(x)
def doc_classification_multilabel_roberta():
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO)
ml_logger = MLFlowLogger(tracking_uri="https://public-mlflow.deepset.ai/")
ml_logger.init_experiment(experiment_name="Public_FARM",
run_name="Run_doc_classification")
##########################
########## Settings
##########################
set_all_seeds(seed=42)
device, n_gpu = initialize_device_settings(use_cuda=False)
n_epochs = 1
batch_size = 32
evaluate_every = 500
lang_model = "roberta-base"
do_lower_case = False # roberta is a cased model
# 1.Create a tokenizer
tokenizer = Tokenizer.load(pretrained_model_name_or_path=lang_model,
do_lower_case=do_lower_case)
# 2. Create a DataProcessor that handles all the conversion from raw text into a pytorch Dataset
# Here we load GermEval 2018 Data.
label_list = [
"toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"
]
metric = "acc"
processor = TextClassificationProcessor(
tokenizer=tokenizer,
max_seq_len=128,
data_dir=Path("../data/toxic-comments"),
label_list=label_list,
label_column_name="label",
metric=metric,
quote_char='"',
multilabel=True,
train_filename=Path("train.tsv"),
dev_filename=Path("val.tsv"),
test_filename=None,
dev_split=0,
max_samples=1000)
# 3. Create a DataSilo that loads several datasets (train/dev/test), provides DataLoaders for them and calculates a few descriptive statistics of our datasets
data_silo = DataSilo(processor=processor, batch_size=batch_size)
# 4. Create an AdaptiveModel
# a) which consists of a pretrained language model as a basis
language_model = Roberta.load(lang_model)
# b) and a prediction head on top that is suited for our task => Text classification
prediction_head = MultiLabelTextClassificationHead(
num_labels=len(label_list))
model = AdaptiveModel(language_model=language_model,
prediction_heads=[prediction_head],
embeds_dropout_prob=0.1,
lm_output_types=["per_sequence"],
device=device)
# 5. Create an optimizer
model, optimizer, lr_schedule = initialize_optimizer(
model=model,
learning_rate=3e-5,
device=device,
n_batches=len(data_silo.loaders["train"]),
n_epochs=n_epochs)
# 6. Feed everything to the Trainer, which keeps care of growing our model into powerful plant and evaluates it from time to time
trainer = Trainer(model=model,
optimizer=optimizer,
data_silo=data_silo,
epochs=n_epochs,
n_gpu=n_gpu,
lr_schedule=lr_schedule,
evaluate_every=evaluate_every,
device=device)
# 7. Let it grow
trainer.train()
# 8. Hooray! You have a model. Store it:
save_dir = Path("saved_models/bert-multi-doc-roberta")
model.save(save_dir)
processor.save(save_dir)
# 9. Load it & harvest your fruits (Inference)
basic_texts = [
{
"text": "You f*****g bastards"
},
{
"text": "What a lovely world"
},
]
model = Inferencer.load(save_dir)
result = model.run_inference(dicts=basic_texts)
print(result)
def test_ner(caplog):
caplog.set_level(logging.CRITICAL)
set_all_seeds(seed=42)
device, n_gpu = initialize_device_settings(use_cuda=False)
n_epochs = 1
batch_size = 8
evaluate_every = 50
lang_model = "bert-base-german-cased"
tokenizer = BertTokenizer.from_pretrained(
pretrained_model_name_or_path=lang_model, do_lower_case=False)
processor = NERProcessor(tokenizer=tokenizer,
max_seq_len=128,
data_dir="samples/ner",
train_filename="train-sample.txt",
dev_filename="dev-sample.txt",
test_filename=None,
delimiter=" ")
data_silo = DataSilo(processor=processor, batch_size=batch_size)
language_model = Bert.load(lang_model)
prediction_head = TokenClassificationHead(
layer_dims=[768, len(processor.label_list)])
model = AdaptiveModel(
language_model=language_model,
prediction_heads=[prediction_head],
embeds_dropout_prob=0.1,
lm_output_types=["per_token"],
device=device,
)
optimizer, warmup_linear = initialize_optimizer(
model=model,
learning_rate=2e-5,
warmup_proportion=0.1,
n_batches=len(data_silo.loaders["train"]),
n_epochs=n_epochs,
)
trainer = Trainer(
optimizer=optimizer,
data_silo=data_silo,
epochs=n_epochs,
n_gpu=n_gpu,
warmup_linear=warmup_linear,
evaluate_every=evaluate_every,
device=device,
)
save_dir = "testsave/ner"
model = trainer.train(model)
model.save(save_dir)
processor.save(save_dir)
basic_texts = [
{
"text":
"Schartau sagte dem Tagesspiegel, dass Fischer ein Idiot sei"
},
]
model = Inferencer.load(save_dir)
result = model.run_inference(dicts=basic_texts)
assert result[0]["predictions"][0]["context"] == "sagte"
assert abs(result[0]["predictions"][0]["probability"] - 0.213869) <= 0.0001
