Source code for flash.text.classification.data
# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# 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.
from typing import Any, Callable, Dict, List, Optional, Sequence, Type, Union
from pandas.core.frame import DataFrame
from flash.core.data.data_module import DataModule
from flash.core.data.io.input import Input
from flash.core.data.io.input_transform import InputTransform
from flash.core.data.utilities.classification import TargetFormatter
from flash.core.data.utilities.paths import PATH_TYPE
from flash.core.integrations.labelstudio.input import LabelStudioTextClassificationInput, _parse_labelstudio_arguments
from flash.core.utilities.imports import _TOPIC_TEXT_AVAILABLE
from flash.core.utilities.stages import RunningStage
from flash.text.classification.input import (
TextClassificationCSVInput,
TextClassificationDataFrameInput,
TextClassificationInput,
TextClassificationJSONInput,
TextClassificationListInput,
TextClassificationParquetInput,
)
if _TOPIC_TEXT_AVAILABLE:
from datasets import Dataset
else:
Dataset = object
# Skip doctests if requirements aren't available
if not _TOPIC_TEXT_AVAILABLE:
__doctest_skip__ = ["TextClassificationData", "TextClassificationData.*"]
[docs]class TextClassificationData(DataModule):
"""The ``TextClassificationData`` class is a :class:`~flash.core.data.data_module.DataModule` with a set of
classmethods for loading data for text classification."""
input_transform_cls = InputTransform
[docs] @classmethod
def from_csv(
cls,
input_field: str,
target_fields: Optional[Union[str, Sequence[str]]] = None,
train_file: Optional[PATH_TYPE] = None,
val_file: Optional[PATH_TYPE] = None,
test_file: Optional[PATH_TYPE] = None,
predict_file: Optional[PATH_TYPE] = None,
target_formatter: Optional[TargetFormatter] = None,
input_cls: Type[Input] = TextClassificationCSVInput,
transform: Optional[Dict[str, Callable]] = InputTransform,
transform_kwargs: Optional[Dict] = None,
**data_module_kwargs: Any,
) -> "TextClassificationData":
"""Load the :class:`~flash.text.classification.data.TextClassificationData` from CSV files containing text
snippets and their corresponding targets.
Input text snippets will be extracted from the ``input_field`` column in the CSV files.
The targets will be extracted from the ``target_fields`` in the CSV files and can be in any of our
:ref:`supported classification target formats <formatting_classification_targets>`.
To learn how to customize the transforms applied for each stage, read our
:ref:`customizing transforms guide <customizing_transforms>`.
Args:
input_field: The field (column name) in the CSV files containing the text snippets.
target_fields: The field (column name) or list of fields in the CSV files containing the targets.
train_file: The CSV file to use when training.
val_file: The CSV file to use when validating.
test_file: The CSV file to use when testing.
predict_file: The CSV file to use when predicting.
target_formatter: Optionally provide a :class:`~flash.core.data.utilities.classification.TargetFormatter` to
control how targets are handled. See :ref:`formatting_classification_targets` for more details.
input_cls: The :class:`~flash.core.data.io.input.Input` type to use for loading the data.
transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use.
transform_kwargs: Dict of keyword arguments to be provided when instantiating the transforms.
data_module_kwargs: Additional keyword arguments to provide to the
:class:`~flash.core.data.data_module.DataModule` constructor.
Returns:
The constructed :class:`~flash.text.classification.data.TextClassificationData`.
Examples
________
The files can be in Comma Separated Values (CSV) format with either a ``.csv`` or ``.txt`` extension.
.. testsetup::
>>> import os
>>> from pandas import DataFrame
>>> DataFrame.from_dict({
... "reviews": ["Best movie ever!", "Not good", "Fine I guess"],
... "targets": ["positive", "negative", "neutral"],
... }).to_csv("train_data.csv", index=False)
>>> DataFrame.from_dict({
... "reviews": ["Worst movie ever!", "I didn't enjoy it", "It was ok"],
... }).to_csv("predict_data.csv", index=False)
The file ``train_data.csv`` contains the following:
.. code-block::
reviews,targets
Best movie ever!,positive
Not good,negative
Fine I guess,neutral
The file ``predict_data.csv`` contains the following:
.. code-block::
reviews
Worst movie ever!
I didn't enjoy it
It was ok
.. doctest::
>>> from flash import Trainer
>>> from flash.text import TextClassifier, TextClassificationData
>>> datamodule = TextClassificationData.from_csv(
... "reviews",
... "targets",
... train_file="train_data.csv",
... predict_file="predict_data.csv",
... batch_size=2,
... )
>>> datamodule.num_classes
3
>>> datamodule.labels
['negative', 'neutral', 'positive']
>>> model = TextClassifier(num_classes=datamodule.num_classes, backbone="prajjwal1/bert-tiny")
>>> trainer = Trainer(fast_dev_run=True)
>>> trainer.fit(model, datamodule=datamodule) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
Training...
>>> trainer.predict(model, datamodule=datamodule) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
Predicting...
.. testcleanup::
>>> os.remove("train_data.csv")
>>> os.remove("predict_data.csv")
Alternatively, the files can be in Tab Separated Values (TSV) format with a ``.tsv`` extension.
.. testsetup::
>>> import os
>>> from pandas import DataFrame
>>> DataFrame.from_dict({
... "reviews": ["Best movie ever!", "Not good", "Fine I guess"],
... "targets": ["positive", "negative", "neutral"],
... }).to_csv("train_data.tsv", sep="\\t", index=False)
>>> DataFrame.from_dict({
... "reviews": ["Worst movie ever!", "I didn't enjoy it", "It was ok"],
... }).to_csv("predict_data.tsv", sep="\\t", index=False)
The file ``train_data.tsv`` contains the following:
.. code-block::
reviews targets
Best movie ever! positive
Not good negative
Fine I guess neutral
The file ``predict_data.tsv`` contains the following:
.. code-block::
reviews
Worst movie ever!
I didn't enjoy it
It was ok
.. doctest::
>>> from flash import Trainer
>>> from flash.text import TextClassifier, TextClassificationData
>>> datamodule = TextClassificationData.from_csv(
... "reviews",
... "targets",
... train_file="train_data.tsv",
... predict_file="predict_data.tsv",
... batch_size=2,
... )
>>> datamodule.num_classes
3
>>> datamodule.labels
['negative', 'neutral', 'positive']
>>> model = TextClassifier(num_classes=datamodule.num_classes, backbone="prajjwal1/bert-tiny")
>>> trainer = Trainer(fast_dev_run=True)
>>> trainer.fit(model, datamodule=datamodule) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
Training...
>>> trainer.predict(model, datamodule=datamodule) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
Predicting...
.. testcleanup::
>>> os.remove("train_data.tsv")
>>> os.remove("predict_data.tsv")
"""
ds_kw = {
"target_formatter": target_formatter,
"input_key": input_field,
"target_keys": target_fields,
}
train_input = input_cls(RunningStage.TRAINING, train_file, **ds_kw)
ds_kw["target_formatter"] = getattr(train_input, "target_formatter", None)
return cls(
train_input,
input_cls(RunningStage.VALIDATING, val_file, **ds_kw),
input_cls(RunningStage.TESTING, test_file, **ds_kw),
input_cls(RunningStage.PREDICTING, predict_file, **ds_kw),
transform=transform,
transform_kwargs=transform_kwargs,
**data_module_kwargs,
)
[docs] @classmethod
def from_json(
cls,
input_field: str,
target_fields: Optional[Union[str, Sequence[str]]] = None,
train_file: Optional[PATH_TYPE] = None,
val_file: Optional[PATH_TYPE] = None,
test_file: Optional[PATH_TYPE] = None,
predict_file: Optional[PATH_TYPE] = None,
target_formatter: Optional[TargetFormatter] = None,
input_cls: Type[Input] = TextClassificationJSONInput,
transform: Optional[Dict[str, Callable]] = InputTransform,
transform_kwargs: Optional[Dict] = None,
field: Optional[str] = None,
**data_module_kwargs: Any,
) -> "TextClassificationData":
"""Load the :class:`~flash.text.classification.data.TextClassificationData` from JSON files containing text
snippets and their corresponding targets.
Input text snippets will be extracted from the ``input_field`` in the JSON objects.
The targets will be extracted from the ``target_fields`` in the JSON objects and can be in any of our
:ref:`supported classification target formats <formatting_classification_targets>`.
To learn how to customize the transforms applied for each stage, read our
:ref:`customizing transforms guide <customizing_transforms>`.
Args:
input_field: The field in the JSON objects containing the text snippets.
target_fields: The field or list of fields in the JSON objects containing the targets.
train_file: The JSON file to use when training.
val_file: The JSON file to use when validating.
test_file: The JSON file to use when testing.
predict_file: The JSON file to use when predicting.
target_formatter: Optionally provide a :class:`~flash.core.data.utilities.classification.TargetFormatter` to
control how targets are handled. See :ref:`formatting_classification_targets` for more details.
input_cls: The :class:`~flash.core.data.io.input.Input` type to use for loading the data.
transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use.
transform_kwargs: Dict of keyword arguments to be provided when instantiating the transforms.
field: To specify the field that holds the data in the JSON file.
data_module_kwargs: Additional keyword arguments to provide to the
:class:`~flash.core.data.data_module.DataModule` constructor.
Returns:
The constructed :class:`~flash.text.classification.data.TextClassificationData`.
Examples
________
.. testsetup::
>>> import os
>>> from pandas import DataFrame
>>> DataFrame.from_dict({
... "reviews": ["Best movie ever!", "Not good", "Fine I guess"],
... "targets": ["positive", "negative", "neutral"],
... }).to_json("train_data.json", orient="records", lines=True)
>>> DataFrame.from_dict({
... "reviews": ["Worst movie ever!", "I didn't enjoy it", "It was ok"],
... }).to_json("predict_data.json", orient="records", lines=True)
The file ``train_data.json`` contains the following:
.. code-block::
{"reviews":"Best movie ever!","targets":"positive"}
{"reviews":"Not good","targets":"negative"}
{"reviews":"Fine I guess","targets":"neutral"}
The file ``predict_data.json`` contains the following:
.. code-block::
{"reviews":"Worst movie ever!"}
{"reviews":"I didn't enjoy it"}
{"reviews":"It was ok"}
.. doctest::
>>> from flash import Trainer
>>> from flash.text import TextClassifier, TextClassificationData
>>> datamodule = TextClassificationData.from_json(
... "reviews",
... "targets",
... train_file="train_data.json",
... predict_file="predict_data.json",
... batch_size=2,
... ) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
<BLANKLINE>
...
>>> datamodule.num_classes
3
>>> datamodule.labels
['negative', 'neutral', 'positive']
>>> model = TextClassifier(num_classes=datamodule.num_classes, backbone="prajjwal1/bert-tiny")
>>> trainer = Trainer(fast_dev_run=True)
>>> trainer.fit(model, datamodule=datamodule) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
Training...
>>> trainer.predict(model, datamodule=datamodule) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
Predicting...
.. testcleanup::
>>> os.remove("train_data.json")
>>> os.remove("predict_data.json")
"""
ds_kw = {
"target_formatter": target_formatter,
"input_key": input_field,
"target_keys": target_fields,
"field": field,
}
train_input = input_cls(RunningStage.TRAINING, train_file, **ds_kw)
ds_kw["target_formatter"] = getattr(train_input, "target_formatter", None)
return cls(
train_input,
input_cls(RunningStage.VALIDATING, val_file, **ds_kw),
input_cls(RunningStage.TESTING, test_file, **ds_kw),
input_cls(RunningStage.PREDICTING, predict_file, **ds_kw),
transform=transform,
transform_kwargs=transform_kwargs,
**data_module_kwargs,
)
[docs] @classmethod
def from_parquet(
cls,
input_field: str,
target_fields: Optional[Union[str, Sequence[str]]] = None,
train_file: Optional[PATH_TYPE] = None,
val_file: Optional[PATH_TYPE] = None,
test_file: Optional[PATH_TYPE] = None,
predict_file: Optional[PATH_TYPE] = None,
target_formatter: Optional[TargetFormatter] = None,
input_cls: Type[Input] = TextClassificationParquetInput,
transform: Optional[Dict[str, Callable]] = InputTransform,
transform_kwargs: Optional[Dict] = None,
**data_module_kwargs: Any,
) -> "TextClassificationData":
"""Load the :class:`~flash.text.classification.data.TextClassificationData` from PARQUET files containing text
snippets and their corresponding targets.
Input text snippets will be extracted from the ``input_field`` column in the PARQUET files.
The targets will be extracted from the ``target_fields`` in the PARQUET files and can be in any of our
:ref:`supported classification target formats <formatting_classification_targets>`.
To learn how to customize the transforms applied for each stage, read our
:ref:`customizing transforms guide <customizing_transforms>`.
Args:
input_field: The field (column name) in the PARQUET files containing the text snippets.
target_fields: The field (column name) or list of fields in the PARQUET files containing the targets.
train_file: The PARQUET file to use when training.
val_file: The PARQUET file to use when validating.
test_file: The PARQUET file to use when testing.
predict_file: The PARQUET file to use when predicting.
target_formatter: Optionally provide a :class:`~flash.core.data.utilities.classification.TargetFormatter` to
control how targets are handled. See :ref:`formatting_classification_targets` for more details.
input_cls: The :class:`~flash.core.data.io.input.Input` type to use for loading the data.
transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use.
transform_kwargs: Dict of keyword arguments to be provided when instantiating the transforms.
data_module_kwargs: Additional keyword arguments to provide to the
:class:`~flash.core.data.data_module.DataModule` constructor.
Returns:
The constructed :class:`~flash.text.classification.data.TextClassificationData`.
Examples
________
.. testsetup::
>>> import os
>>> from pandas import DataFrame
>>> DataFrame.from_dict({
... "reviews": ["Best movie ever!", "Not good", "Fine I guess"],
... "targets": ["positive", "negative", "neutral"],
... }).to_parquet("train_data.parquet", index=False)
>>> DataFrame.from_dict({
... "reviews": ["Worst movie ever!", "I didn't enjoy it", "It was ok"],
... }).to_parquet("predict_data.parquet", index=False)
The file ``train_data.parquet`` contains the following contents encoded in the PARQUET format:
.. code-block::
reviews,targets
Best movie ever!,positive
Not good,negative
Fine I guess,neutral
The file ``predict_data.parquet`` contains the following contents encoded in the PARQUET format:
.. code-block::
reviews
Worst movie ever!
I didn't enjoy it
It was ok
.. doctest::
>>> from flash import Trainer
>>> from flash.text import TextClassifier, TextClassificationData
>>> datamodule = TextClassificationData.from_parquet(
... "reviews",
... "targets",
... train_file="train_data.parquet",
... predict_file="predict_data.parquet",
... batch_size=2,
... ) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
<BLANKLINE>
...
>>> datamodule.num_classes
3
>>> datamodule.labels
['negative', 'neutral', 'positive']
>>> model = TextClassifier(num_classes=datamodule.num_classes, backbone="prajjwal1/bert-tiny")
>>> trainer = Trainer(fast_dev_run=True)
>>> trainer.fit(model, datamodule=datamodule) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
Training...
>>> trainer.predict(model, datamodule=datamodule) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
Predicting...
.. testcleanup::
>>> os.remove("train_data.parquet")
>>> os.remove("predict_data.parquet")
"""
ds_kw = {
"target_formatter": target_formatter,
"input_key": input_field,
"target_keys": target_fields,
}
train_input = input_cls(RunningStage.TRAINING, train_file, **ds_kw)
ds_kw["target_formatter"] = getattr(train_input, "target_formatter", None)
return cls(
train_input,
input_cls(RunningStage.VALIDATING, val_file, **ds_kw),
input_cls(RunningStage.TESTING, test_file, **ds_kw),
input_cls(RunningStage.PREDICTING, predict_file, **ds_kw),
transform=transform,
transform_kwargs=transform_kwargs,
**data_module_kwargs,
)
[docs] @classmethod
def from_hf_datasets(
cls,
input_field: str,
target_fields: Optional[Union[str, Sequence[str]]] = None,
train_hf_dataset: Optional[Dataset] = None,
val_hf_dataset: Optional[Dataset] = None,
test_hf_dataset: Optional[Dataset] = None,
predict_hf_dataset: Optional[Dataset] = None,
target_formatter: Optional[TargetFormatter] = None,
input_cls: Type[Input] = TextClassificationInput,
transform: Optional[Dict[str, Callable]] = InputTransform,
transform_kwargs: Optional[Dict] = None,
**data_module_kwargs: Any,
) -> "TextClassificationData":
"""Load the :class:`~flash.text.classification.data.TextClassificationData` from Hugging Face ``Dataset``
objects containing text snippets and their corresponding targets.
Input text snippets will be extracted from the ``input_field`` column in the ``Dataset`` objects.
The targets will be extracted from the ``target_fields`` in the ``Dataset`` objects and can be in any of our
:ref:`supported classification target formats <formatting_classification_targets>`.
To learn how to customize the transforms applied for each stage, read our
:ref:`customizing transforms guide <customizing_transforms>`.
Args:
input_field: The field (column name) in the ``Dataset`` objects containing the text snippets.
target_fields: The field (column name) or list of fields in the ``Dataset`` objects containing the targets.
train_hf_dataset: The ``Dataset`` to use when training.
val_hf_dataset: The ``Dataset`` to use when validating.
test_hf_dataset: The ``Dataset`` to use when testing.
predict_hf_dataset: The ``Dataset`` to use when predicting.
target_formatter: Optionally provide a :class:`~flash.core.data.utilities.classification.TargetFormatter` to
control how targets are handled. See :ref:`formatting_classification_targets` for more details.
input_cls: The :class:`~flash.core.data.io.input.Input` type to use for loading the data.
transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use.
transform_kwargs: Dict of keyword arguments to be provided when instantiating the transforms.
data_module_kwargs: Additional keyword arguments to provide to the
:class:`~flash.core.data.data_module.DataModule` constructor.
Returns:
The constructed :class:`~flash.text.classification.data.TextClassificationData`.
Examples
________
.. doctest::
>>> from datasets import Dataset
>>> from flash import Trainer
>>> from flash.text import TextClassifier, TextClassificationData
>>> train_data = Dataset.from_dict(
... {
... "reviews": ["Best movie ever!", "Not good", "Fine I guess"],
... "targets": ["positive", "negative", "neutral"],
... }
... )
>>> predict_data = Dataset.from_dict(
... {
... "reviews": ["Worst movie ever!", "I didn't enjoy it", "It was ok"],
... }
... )
>>> datamodule = TextClassificationData.from_hf_datasets(
... "reviews",
... "targets",
... train_hf_dataset=train_data,
... predict_hf_dataset=predict_data,
... batch_size=2,
... )
>>> datamodule.num_classes
3
>>> datamodule.labels
['negative', 'neutral', 'positive']
>>> model = TextClassifier(num_classes=datamodule.num_classes, backbone="prajjwal1/bert-tiny")
>>> trainer = Trainer(fast_dev_run=True)
>>> trainer.fit(model, datamodule=datamodule) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
Training...
>>> trainer.predict(model, datamodule=datamodule) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
Predicting...
.. testcleanup::
>>> del train_data
>>> del predict_data
"""
ds_kw = {
"target_formatter": target_formatter,
"input_key": input_field,
"target_keys": target_fields,
}
train_input = input_cls(RunningStage.TRAINING, train_hf_dataset, **ds_kw)
ds_kw["target_formatter"] = getattr(train_input, "target_formatter", None)
return cls(
train_input,
input_cls(RunningStage.VALIDATING, val_hf_dataset, **ds_kw),
input_cls(RunningStage.TESTING, test_hf_dataset, **ds_kw),
input_cls(RunningStage.PREDICTING, predict_hf_dataset, **ds_kw),
transform=transform,
transform_kwargs=transform_kwargs,
**data_module_kwargs,
)
[docs] @classmethod
def from_data_frame(
cls,
input_field: str,
target_fields: Optional[Union[str, Sequence[str]]] = None,
train_data_frame: Optional[DataFrame] = None,
val_data_frame: Optional[DataFrame] = None,
test_data_frame: Optional[DataFrame] = None,
predict_data_frame: Optional[DataFrame] = None,
target_formatter: Optional[TargetFormatter] = None,
input_cls: Type[Input] = TextClassificationDataFrameInput,
transform: Optional[Dict[str, Callable]] = InputTransform,
transform_kwargs: Optional[Dict] = None,
**data_module_kwargs: Any,
) -> "TextClassificationData":
"""Load the :class:`~flash.text.classification.data.TextClassificationData` from Pandas ``DataFrame`` objects
containing text snippets and their corresponding targets.
Input text snippets will be extracted from the ``input_field`` column in the ``DataFrame`` objects.
The targets will be extracted from the ``target_fields`` in the ``DataFrame`` objects and can be in any of our
:ref:`supported classification target formats <formatting_classification_targets>`.
To learn how to customize the transforms applied for each stage, read our
:ref:`customizing transforms guide <customizing_transforms>`.
Args:
input_field: The field (column name) in the ``DataFrame`` objects containing the text snippets.
target_fields: The field (column name) or list of fields in the ``DataFrame`` objects containing the
targets.
train_data_frame: The ``DataFrame`` to use when training.
val_data_frame: The ``DataFrame`` to use when validating.
test_data_frame: The ``DataFrame`` to use when testing.
predict_data_frame: The ``DataFrame`` to use when predicting.
target_formatter: Optionally provide a :class:`~flash.core.data.utilities.classification.TargetFormatter` to
control how targets are handled. See :ref:`formatting_classification_targets` for more details.
input_cls: The :class:`~flash.core.data.io.input.Input` type to use for loading the data.
transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use.
transform_kwargs: Dict of keyword arguments to be provided when instantiating the transforms.
data_module_kwargs: Additional keyword arguments to provide to the
:class:`~flash.core.data.data_module.DataModule` constructor.
Returns:
The constructed :class:`~flash.text.classification.data.TextClassificationData`.
Examples
________
.. doctest::
>>> from pandas import DataFrame
>>> from flash import Trainer
>>> from flash.text import TextClassifier, TextClassificationData
>>> train_data = DataFrame.from_dict(
... {
... "reviews": ["Best movie ever!", "Not good", "Fine I guess"],
... "targets": ["positive", "negative", "neutral"],
... }
... )
>>> predict_data = DataFrame.from_dict(
... {
... "reviews": ["Worst movie ever!", "I didn't enjoy it", "It was ok"],
... }
... )
>>> datamodule = TextClassificationData.from_data_frame(
... "reviews",
... "targets",
... train_data_frame=train_data,
... predict_data_frame=predict_data,
... batch_size=2,
... )
>>> datamodule.num_classes
3
>>> datamodule.labels
['negative', 'neutral', 'positive']
>>> model = TextClassifier(num_classes=datamodule.num_classes, backbone="prajjwal1/bert-tiny")
>>> trainer = Trainer(fast_dev_run=True)
>>> trainer.fit(model, datamodule=datamodule) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
Training...
>>> trainer.predict(model, datamodule=datamodule) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
Predicting...
.. testcleanup::
>>> del train_data
>>> del predict_data
"""
ds_kw = {
"target_formatter": target_formatter,
"input_key": input_field,
"target_keys": target_fields,
}
train_input = input_cls(RunningStage.TRAINING, train_data_frame, **ds_kw)
ds_kw["target_formatter"] = getattr(train_input, "target_formatter", None)
return cls(
train_input,
input_cls(RunningStage.VALIDATING, val_data_frame, **ds_kw),
input_cls(RunningStage.TESTING, test_data_frame, **ds_kw),
input_cls(RunningStage.PREDICTING, predict_data_frame, **ds_kw),
transform=transform,
transform_kwargs=transform_kwargs,
**data_module_kwargs,
)
[docs] @classmethod
def from_lists(
cls,
train_data: Optional[List[str]] = None,
train_targets: Optional[Union[List[Any], List[List[Any]]]] = None,
val_data: Optional[List[str]] = None,
val_targets: Optional[Union[List[Any], List[List[Any]]]] = None,
test_data: Optional[List[str]] = None,
test_targets: Optional[Union[List[Any], List[List[Any]]]] = None,
predict_data: Optional[List[str]] = None,
target_formatter: Optional[TargetFormatter] = None,
input_cls: Type[Input] = TextClassificationListInput,
transform: Optional[Dict[str, Callable]] = InputTransform,
transform_kwargs: Optional[Dict] = None,
**data_module_kwargs: Any,
) -> "TextClassificationData":
"""Load the :class:`~flash.text.classification.data.TextClassificationData` from lists of text snippets and
corresponding lists of targets.
The targets can be in any of our
:ref:`supported classification target formats <formatting_classification_targets>`.
To learn how to customize the transforms applied for each stage, read our
:ref:`customizing transforms guide <customizing_transforms>`.
Args:
train_data: The list of text snippets to use when training.
train_targets: The list of targets to use when training.
val_data: The list of text snippets to use when validating.
val_targets: The list of targets to use when validating.
test_data: The list of text snippets to use when testing.
test_targets: The list of targets to use when testing.
predict_data: The list of text snippets to use when predicting.
target_formatter: Optionally provide a :class:`~flash.core.data.utilities.classification.TargetFormatter` to
control how targets are handled. See :ref:`formatting_classification_targets` for more details.
input_cls: The :class:`~flash.core.data.io.input.Input` type to use for loading the data.
transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use.
transform_kwargs: Dict of keyword arguments to be provided when instantiating the transforms.
data_module_kwargs: Additional keyword arguments to provide to the
:class:`~flash.core.data.data_module.DataModule` constructor.
Returns:
The constructed :class:`~flash.text.classification.data.TextClassificationData`.
Examples
________
.. doctest::
>>> from flash import Trainer
>>> from flash.text import TextClassifier, TextClassificationData
>>> datamodule = TextClassificationData.from_lists(
... train_data=["Best movie ever!", "Not good", "Fine I guess"],
... train_targets=["positive", "negative", "neutral"],
... predict_data=["Worst movie ever!", "I didn't enjoy it", "It was ok"],
... batch_size=2,
... )
>>> datamodule.num_classes
3
>>> datamodule.labels
['negative', 'neutral', 'positive']
>>> model = TextClassifier(num_classes=datamodule.num_classes, backbone="prajjwal1/bert-tiny")
>>> trainer = Trainer(fast_dev_run=True)
>>> trainer.fit(model, datamodule=datamodule) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
Training...
>>> trainer.predict(model, datamodule=datamodule) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
Predicting...
"""
ds_kw = {
"target_formatter": target_formatter,
}
train_input = input_cls(RunningStage.TRAINING, train_data, train_targets, **ds_kw)
ds_kw["target_formatter"] = getattr(train_input, "target_formatter", None)
return cls(
train_input,
input_cls(RunningStage.VALIDATING, val_data, val_targets, **ds_kw),
input_cls(RunningStage.TESTING, test_data, test_targets, **ds_kw),
input_cls(RunningStage.PREDICTING, predict_data, **ds_kw),
transform=transform,
transform_kwargs=transform_kwargs,
**data_module_kwargs,
)
[docs] @classmethod
def from_labelstudio(
cls,
export_json: str = None,
train_export_json: str = None,
val_export_json: str = None,
test_export_json: str = None,
predict_export_json: str = None,
data_folder: str = None,
train_data_folder: str = None,
val_data_folder: str = None,
test_data_folder: str = None,
predict_data_folder: str = None,
input_cls: Type[Input] = LabelStudioTextClassificationInput,
transform: Optional[Dict[str, Callable]] = InputTransform,
transform_kwargs: Optional[Dict] = None,
val_split: Optional[float] = None,
multi_label: Optional[bool] = False,
**data_module_kwargs: Any,
) -> "TextClassificationData":
"""Creates a :class:`~flash.core.data.data_module.DataModule` object from the given export file and data
directory using the :class:`~flash.core.data.io.input.Input` of name
:attr:`~flash.core.data.io.input.InputFormat.FOLDERS` from the passed or constructed
:class:`~flash.core.data.io.input_transform.InputTransform`.
Args:
export_json: path to label studio export file
train_export_json: path to label studio export file for train set, overrides export_json if specified
val_export_json: path to label studio export file for validation
test_export_json: path to label studio export file for test
predict_export_json: path to label studio export file for predict
data_folder: path to label studio data folder
train_data_folder: path to label studio data folder for train data set, overrides data_folder if specified
val_data_folder: path to label studio data folder for validation data
test_data_folder: path to label studio data folder for test data
predict_data_folder: path to label studio data folder for predict data
input_cls: The :class:`~flash.core.data.io.input.Input` type to use for loading the data.
transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use.
transform_kwargs: Dict of keyword arguments to be provided when instantiating the transforms.
val_split: The ``val_split`` argument to pass to the :class:`~flash.core.data.data_module.DataModule`.
multi_label: Whether the labels are multi encoded.
data_module_kwargs: Additional keyword arguments to use when constructing the datamodule.
Returns:
The constructed data module.
"""
train_data, val_data, test_data, predict_data = _parse_labelstudio_arguments(
export_json=export_json,
train_export_json=train_export_json,
val_export_json=val_export_json,
test_export_json=test_export_json,
predict_export_json=predict_export_json,
data_folder=data_folder,
train_data_folder=train_data_folder,
val_data_folder=val_data_folder,
test_data_folder=test_data_folder,
predict_data_folder=predict_data_folder,
val_split=val_split,
multi_label=multi_label,
)
ds_kw = {}
train_input = input_cls(RunningStage.TRAINING, train_data, **ds_kw)
ds_kw["parameters"] = getattr(train_input, "parameters", None)
return cls(
train_input,
input_cls(RunningStage.VALIDATING, val_data, **ds_kw),
input_cls(RunningStage.TESTING, test_data, **ds_kw),
input_cls(RunningStage.PREDICTING, predict_data, **ds_kw),
transform=transform,
transform_kwargs=transform_kwargs,
**data_module_kwargs,
)
