def test_from_csv_multilabel(tmpdir):
csv_path = csv_data(tmpdir, multilabel=True)
dm = TextClassificationData.from_csv(
"sentence",
["lab1", "lab2"],
train_file=csv_path,
val_file=csv_path,
test_file=csv_path,
predict_file=csv_path,
batch_size=1,
)
assert dm.multi_label
batch = next(iter(dm.train_dataloader()))
assert all([label in [0, 1] for label in batch[DataKeys.TARGET][0]])
assert isinstance(batch[DataKeys.INPUT][0], str)
batch = next(iter(dm.val_dataloader()))
assert all([label in [0, 1] for label in batch[DataKeys.TARGET][0]])
assert isinstance(batch[DataKeys.INPUT][0], str)
batch = next(iter(dm.test_dataloader()))
assert all([label in [0, 1] for label in batch[DataKeys.TARGET][0]])
assert isinstance(batch[DataKeys.INPUT][0], str)
batch = next(iter(dm.predict_dataloader()))
assert isinstance(batch[DataKeys.INPUT][0], str)
def test_from_csv(tmpdir):
csv_path = csv_data(tmpdir, multilabel=False)
dm = TextClassificationData.from_csv(
"sentence",
"label",
train_file=csv_path,
val_file=csv_path,
test_file=csv_path,
predict_file=csv_path,
batch_size=1,
)
batch = next(iter(dm.train_dataloader()))
assert batch[DataKeys.TARGET].item() in [0, 1]
assert isinstance(batch[DataKeys.INPUT][0], str)
batch = next(iter(dm.val_dataloader()))
assert batch[DataKeys.TARGET].item() in [0, 1]
assert isinstance(batch[DataKeys.INPUT][0], str)
batch = next(iter(dm.test_dataloader()))
assert batch[DataKeys.TARGET].item() in [0, 1]
assert isinstance(batch[DataKeys.INPUT][0], str)
batch = next(iter(dm.predict_dataloader()))
assert isinstance(batch[DataKeys.INPUT][0], str)
def test_from_csv(tmpdir):
csv_path = csv_data(tmpdir)
dm = TextClassificationData.from_csv("sentence",
"label",
backbone=TEST_BACKBONE,
train_file=csv_path,
batch_size=1)
batch = next(iter(dm.train_dataloader()))
assert batch["labels"].item() in [0, 1]
assert "input_ids" in batch
def from_imdb(
batch_size: int = 4,
**data_module_kwargs,
) -> TextClassificationData:
"""Downloads and loads the IMDB sentiment classification data set."""
download_data("https://pl-flash-data.s3.amazonaws.com/imdb.zip", "./data/")
return TextClassificationData.from_csv(
"review",
"sentiment",
train_file="data/imdb/train.csv",
val_file="data/imdb/valid.csv",
batch_size=batch_size,
**data_module_kwargs,
)
def test_classification(tmpdir):
csv_path = csv_data(tmpdir)
data = TextClassificationData.from_csv(
"sentence",
"label",
train_file=csv_path,
num_workers=0,
batch_size=2,
)
model = TextClassifier(2, TEST_BACKBONE)
trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True)
trainer.fit(model, datamodule=data)
def from_toxic(
val_split: float = 0.1,
batch_size: int = 4,
**data_module_kwargs,
) -> TextClassificationData:
"""Downloads and loads the Jigsaw toxic comments data set."""
download_data("https://pl-flash-data.s3.amazonaws.com/jigsaw_toxic_comments.zip", "./data")
return TextClassificationData.from_csv(
"comment_text",
["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"],
train_file="data/jigsaw_toxic_comments/train.csv",
val_split=val_split,
batch_size=batch_size,
**data_module_kwargs,
)
def from_imdb(
backbone: str = "prajjwal1/bert-medium",
batch_size: int = 4,
num_workers: int = 0,
**preprocess_kwargs,
) -> TextClassificationData:
"""Downloads and loads the IMDB sentiment classification data set."""
download_data("https://pl-flash-data.s3.amazonaws.com/imdb.zip", "./data/")
return TextClassificationData.from_csv(
"review",
"sentiment",
train_file="data/imdb/train.csv",
val_file="data/imdb/valid.csv",
backbone=backbone,
batch_size=batch_size,
num_workers=num_workers,
**preprocess_kwargs,
)
def from_toxic(
backbone: str = "unitary/toxic-bert",
val_split: float = 0.1,
batch_size: int = 4,
num_workers: int = 0,
**preprocess_kwargs,
) -> TextClassificationData:
"""Downloads and loads the Jigsaw toxic comments data set."""
download_data("https://pl-flash-data.s3.amazonaws.com/jigsaw_toxic_comments.zip", "./data")
return TextClassificationData.from_csv(
"comment_text",
["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"],
train_file="data/jigsaw_toxic_comments/train.csv",
backbone=backbone,
val_split=val_split,
batch_size=batch_size,
num_workers=num_workers,
**preprocess_kwargs,
)
from flash.core.data.utils import download_data
from flash.text import TextClassificationData, TextClassifier
# 1. Download the data from the Kaggle Toxic Comment Classification Challenge:
# https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge
download_data(
"https://pl-flash-data.s3.amazonaws.com/jigsaw_toxic_comments.zip",
"data/")
# 2. Load the model from a checkpoint
model = TextClassifier.load_from_checkpoint(
"https://flash-weights.s3.amazonaws.com/text_classification_multi_label_model.pt"
)
# 2a. Classify a few sentences! How was the movie?
predictions = model.predict([
"No, he is an arrogant, self serving, immature idiot. Get it right.",
"U SUCK HANNAH MONTANA",
"Would you care to vote? Thx.",
])
print(predictions)
# 2b. Or generate predictions from a whole file!
datamodule = TextClassificationData.from_csv(
"comment_text",
predict_file="data/jigsaw_toxic_comments/predict.csv",
)
predictions = Trainer().predict(model, datamodule=datamodule)
print(predictions)
from flash.core.classification import Labels
from flash.core.data.utils import download_data
from flash.text import TextClassificationData, TextClassifier
# 1. Download the data
download_data("https://pl-flash-data.s3.amazonaws.com/imdb.zip", "data/")
# 2. Load the model from a checkpoint
model = TextClassifier.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/text_classification_model.pt")
model.serializer = Labels()
# 2a. Classify a few sentences! How was the movie?
predictions = model.predict([
"Turgid dialogue, feeble characterization - Harvey Keitel a judge?.",
"The worst movie in the history of cinema.",
"I come from Bulgaria where it 's almost impossible to have a tornado.",
"Very, very afraid.",
"This guy has done a great job with this movie!",
])
print(predictions)
# 2b. Or generate predictions from a sheet file!
datamodule = TextClassificationData.from_csv(
"review",
predict_file="data/imdb/predict.csv",
)
predictions = Trainer().predict(model, datamodule=datamodule)
print(predictions)
import flash
from flash.core.data.utils import download_data
from flash.text import TextClassificationData, TextClassifier
# 1. Download the data from the Kaggle Toxic Comment Classification Challenge:
# https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge
download_data(
"https://pl-flash-data.s3.amazonaws.com/jigsaw_toxic_comments.zip",
"data/")
# 2. Load the data
datamodule = TextClassificationData.from_csv(
"comment_text",
["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"],
train_file="data/jigsaw_toxic_comments/train.csv",
test_file="data/jigsaw_toxic_comments/test.csv",
predict_file="data/jigsaw_toxic_comments/predict.csv",
batch_size=16,
val_split=0.1,
backbone="unitary/toxic-bert",
)
# 3. Build the model
model = TextClassifier(
num_classes=datamodule.num_classes,
multi_label=True,
metrics=F1(num_classes=datamodule.num_classes),
backbone="unitary/toxic-bert",
)
# 4. Create the trainer
trainer = flash.Trainer(fast_dev_run=True)
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import flash
from flash.data.utils import download_data
from flash.text import TextClassificationData, TextClassifier
# 1. Download the data
download_data("https://pl-flash-data.s3.amazonaws.com/imdb.zip", "data/")
# 2. Load the data
datamodule = TextClassificationData.from_csv(
train_file="data/imdb/train.csv",
val_file="data/imdb/valid.csv",
test_file="data/imdb/test.csv",
input_fields="review",
target_fields="sentiment",
batch_size=16,
)
# 3. Build the model
model = TextClassifier(num_classes=datamodule.num_classes)
# 4. Create the trainer
trainer = flash.Trainer(fast_dev_run=True)
# 5. Fine-tune the model
trainer.finetune(model, datamodule=datamodule, strategy="freeze")
# 6. Test model
trainer.test(model)
import flash
from flash.core.data.utils import download_data
from flash.text import TextClassificationData, TextClassifier
# 1. Create the DataModule
# Data from the Kaggle Toxic Comment Classification Challenge:
# https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge
download_data(
"https://pl-flash-data.s3.amazonaws.com/jigsaw_toxic_comments.zip",
"./data")
datamodule = TextClassificationData.from_csv(
"comment_text",
["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"],
train_file="data/jigsaw_toxic_comments/train.csv",
val_split=0.1,
batch_size=4,
)
# 2. Build the task
model = TextClassifier(
backbone="unitary/toxic-bert",
labels=datamodule.labels,
multi_label=datamodule.multi_label,
)
# 3. Create the trainer and finetune the model
trainer = flash.Trainer(max_epochs=1, gpus=torch.cuda.device_count())
trainer.finetune(model, datamodule=datamodule, strategy="freeze")
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import flash
from flash.core.data.utils import download_data
from flash.text import TextClassificationData, TextClassifier
# 1. Create the DataModule
download_data("https://pl-flash-data.s3.amazonaws.com/imdb.zip", "./data/")
datamodule = TextClassificationData.from_csv(
"review",
"sentiment",
train_file="data/imdb/train.csv",
val_file="data/imdb/valid.csv",
batch_size=4,
)
# 2. Build the task
model = TextClassifier(backbone="prajjwal1/bert-medium",
labels=datamodule.labels)
# 3. Create the trainer and finetune the model
trainer = flash.Trainer(max_epochs=3, gpus=torch.cuda.device_count())
trainer.finetune(model, datamodule=datamodule, strategy="freeze")
# 4. Classify a few sentences! How was the movie?
datamodule = TextClassificationData.from_lists(
predict_data=[
