Source code for flash.text.question_answering.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, Dict, Optional, Type
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.paths import PATH_TYPE
from flash.core.utilities.imports import _TEXT_AVAILABLE
from flash.core.utilities.stages import RunningStage
from flash.core.utilities.types import INPUT_TRANSFORM_TYPE
from flash.text.question_answering.input import (
QuestionAnsweringCSVInput,
QuestionAnsweringDictionaryInput,
QuestionAnsweringJSONInput,
QuestionAnsweringSQuADInput,
)
# Skip doctests if requirements aren't available
if not _TEXT_AVAILABLE:
__doctest_skip__ = ["QuestionAnsweringData", "QuestionAnsweringData.*"]
[docs]class QuestionAnsweringData(DataModule):
"""The ``QuestionAnsweringData`` class is a :class:`~flash.core.data.data_module.DataModule` with a set of
classmethods for loading data for extractive question answering."""
input_transform_cls = InputTransform
[docs] @classmethod
def from_csv(
cls,
train_file: Optional[PATH_TYPE] = None,
val_file: Optional[PATH_TYPE] = None,
test_file: Optional[PATH_TYPE] = None,
predict_file: Optional[PATH_TYPE] = None,
train_transform: INPUT_TRANSFORM_TYPE = InputTransform,
val_transform: INPUT_TRANSFORM_TYPE = InputTransform,
test_transform: INPUT_TRANSFORM_TYPE = InputTransform,
predict_transform: INPUT_TRANSFORM_TYPE = InputTransform,
input_cls: Type[Input] = QuestionAnsweringCSVInput,
transform_kwargs: Optional[Dict] = None,
question_column_name: str = "question",
context_column_name: str = "context",
answer_column_name: str = "answer",
**data_module_kwargs: Any,
) -> "QuestionAnsweringData":
"""Load the :class:`~flash.text.question_answering.data.QuestionAnsweringData` from CSV files containing
questions, contexts and their corresponding answers.
Question snippets will be extracted from the ``question_column_name`` column in the CSV files.
Context snippets will be extracted from the ``context_column_name`` column in the CSV files.
Answer snippets will be extracted from the ``answer_column_name`` column in the CSV files.
To learn how to customize the transforms applied for each stage, read our
:ref:`customizing transforms guide <customizing_transforms>`.
Args:
train_file: The CSV file containing the training data.
val_file: The CSV file containing the validation data.
test_file: The CSV file containing the testing data.
predict_file: The CSV file containing the data to use when predicting.
train_transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use when training.
val_transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use when validating.
test_transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use when testing.
predict_transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use when
predicting.
input_cls: The :class:`~flash.core.data.io.input.Input` type to use for loading the data.
transform_kwargs: Dict of keyword arguments to be provided when instantiating the transforms.
question_column_name: The key in the JSON file to recognize the question field.
context_column_name: The key in the JSON file to recognize the context field.
answer_column_name: The key in the JSON file to recognize the answer field.
Returns:
The constructed :class:`~flash.text.question_answering.data.QuestionAnsweringData`.
Examples
________
.. testsetup::
>>> import os
>>> from pandas import DataFrame
>>> DataFrame.from_dict({
... "id": ["12345", "12346", "12347", "12348"],
... "context": [
... "this is an answer one. this is a context one",
... "this is an answer two. this is a context two",
... "this is an answer three. this is a context three",
... "this is an answer four. this is a context four",
... ],
... "question": [
... "this is a question one",
... "this is a question two",
... "this is a question three",
... "this is a question four",
... ],
... "answer_text": [
... "this is an answer one",
... "this is an answer two",
... "this is an answer three",
... "this is an answer four",
... ],
... "answer_start": [0, 0, 0, 0],
... }).to_csv("train_data.csv", index=False)
>>> DataFrame.from_dict({
... "id": ["12349", "12350"],
... "context": [
... "this is an answer five. this is a context five",
... "this is an answer six. this is a context six",
... ],
... "question": [
... "this is a question five",
... "this is a question six",
... ],
... }).to_csv("predict_data.csv", index=False)
The file ``train_data.csv`` contains the following:
.. code-block::
id,context,question,answer_text,answer_start
12345,this is an answer one. this is a context one,this is a question one,this is an answer one,0
12346,this is an answer two. this is a context two,this is a question two,this is an answer two,0
12347,this is an answer three. this is a context three,this is a question three,this is an answer three,0
12348,this is an answer four. this is a context four,this is a question four,this is an answer four,0
The file ``predict_data.csv`` contains the following:
.. code-block::
id,context,question
12349,this is an answer five. this is a context five,this is a question five
12350,this is an answer six. this is a context six,this is a question six
.. doctest::
>>> from flash import Trainer
>>> from flash.text import QuestionAnsweringData, QuestionAnsweringTask
>>> datamodule = QuestionAnsweringData.from_csv(
... train_file="train_data.csv",
... predict_file="predict_data.csv",
... batch_size=2,
... ) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
Downloading...
>>> model = QuestionAnsweringTask(max_source_length=32, max_target_length=32)
>>> 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")
"""
ds_kw = dict(
question_column_name=question_column_name,
context_column_name=context_column_name,
answer_column_name=answer_column_name,
transform_kwargs=transform_kwargs,
input_transforms_registry=cls.input_transforms_registry,
)
return cls(
input_cls(RunningStage.TRAINING, train_file, transform=train_transform, **ds_kw),
input_cls(RunningStage.VALIDATING, val_file, transform=val_transform, **ds_kw),
input_cls(RunningStage.TESTING, test_file, transform=test_transform, **ds_kw),
input_cls(RunningStage.PREDICTING, predict_file, transform=predict_transform, **ds_kw),
**data_module_kwargs,
)
[docs] @classmethod
def from_json(
cls,
train_file: Optional[PATH_TYPE] = None,
val_file: Optional[PATH_TYPE] = None,
test_file: Optional[PATH_TYPE] = None,
predict_file: Optional[PATH_TYPE] = None,
train_transform: INPUT_TRANSFORM_TYPE = InputTransform,
val_transform: INPUT_TRANSFORM_TYPE = InputTransform,
test_transform: INPUT_TRANSFORM_TYPE = InputTransform,
predict_transform: INPUT_TRANSFORM_TYPE = InputTransform,
input_cls: Type[Input] = QuestionAnsweringJSONInput,
transform_kwargs: Optional[Dict] = None,
field: Optional[str] = None,
question_column_name: str = "question",
context_column_name: str = "context",
answer_column_name: str = "answer",
**data_module_kwargs: Any,
) -> "QuestionAnsweringData":
"""Load the :class:`~flash.text.question_answering.data.QuestionAnsweringData` from JSON files containing
questions, contexts and their corresponding answers.
Question snippets will be extracted from the ``question_column_name`` column in the JSON files.
Context snippets will be extracted from the ``context_column_name`` column in the JSON files.
Answer snippets will be extracted from the ``answer_column_name`` column in the JSON files.
To learn how to customize the transforms applied for each stage, read our
:ref:`customizing transforms guide <customizing_transforms>`.
Args:
train_file: The JSON file containing the training data.
val_file: The JSON file containing the validation data.
test_file: The JSON file containing the testing data.
predict_file: The JSON file containing the data to use when predicting.
train_transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use when training.
val_transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use when validating.
test_transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use when testing.
predict_transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use when
predicting.
input_cls: The :class:`~flash.core.data.io.input.Input` type to use for loading the data.
transform_kwargs: Dict of keyword arguments to be provided when instantiating the transforms.
field: The field that holds the data in the JSON file.
question_column_name: The key in the JSON file to recognize the question field.
context_column_name: The key in the JSON file to recognize the context field.
answer_column_name: The key in the JSON file to recognize the answer field.
Returns:
The constructed :class:`~flash.text.question_answering.data.QuestionAnsweringData`.
Examples
________
.. testsetup::
>>> import os
>>> from pandas import DataFrame
>>> DataFrame.from_dict({
... "id": ["12345", "12346", "12347", "12348"],
... "context": [
... "this is an answer one. this is a context one",
... "this is an answer two. this is a context two",
... "this is an answer three. this is a context three",
... "this is an answer four. this is a context four",
... ],
... "question": [
... "this is a question one",
... "this is a question two",
... "this is a question three",
... "this is a question four",
... ],
... "answer_text": [
... "this is an answer one",
... "this is an answer two",
... "this is an answer three",
... "this is an answer four",
... ],
... "answer_start": [0, 0, 0, 0],
... }).to_json("train_data.json", orient="records", lines=True)
>>> DataFrame.from_dict({
... "id": ["12349", "12350"],
... "context": [
... "this is an answer five. this is a context five",
... "this is an answer six. this is a context six",
... ],
... "question": [
... "this is a question five",
... "this is a question six",
... ],
... }).to_json("predict_data.json", orient="records", lines=True)
The file ``train_data.json`` contains the following:
.. code-block::
{"id":"12345","context":"this is an answer one. this is a context one","question":"this is a question one",
"answer_text":"this is an answer one","answer_start":0}
{"id":"12346","context":"this is an answer two. this is a context two","question":"this is a question two",
"answer_text":"this is an answer two","answer_start":0}
{"id":"12347","context":"this is an answer three. this is a context three","question":"this is a question
three","answer_text":"this is an answer three","answer_start":0}
{"id":"12348","context":"this is an answer four. this is a context four","question":"this is a question
four","answer_text":"this is an answer four","answer_start":0}
The file ``predict_data.json`` contains the following:
.. code-block::
{"id":"12349","context":"this is an answer five. this is a context five","question":"this is a question
five"}
{"id":"12350","context":"this is an answer six. this is a context six","question":"this is a question
six"}
.. doctest::
>>> from flash import Trainer
>>> from flash.text import QuestionAnsweringData, QuestionAnsweringTask
>>> datamodule = QuestionAnsweringData.from_json(
... train_file="train_data.json",
... predict_file="predict_data.json",
... batch_size=2,
... ) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
Downloading...
>>> model = QuestionAnsweringTask(max_source_length=32, max_target_length=32)
>>> 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 = dict(
field=field,
question_column_name=question_column_name,
context_column_name=context_column_name,
answer_column_name=answer_column_name,
transform_kwargs=transform_kwargs,
input_transforms_registry=cls.input_transforms_registry,
)
return cls(
input_cls(RunningStage.TRAINING, train_file, transform=train_transform, **ds_kw),
input_cls(RunningStage.VALIDATING, val_file, transform=val_transform, **ds_kw),
input_cls(RunningStage.TESTING, test_file, transform=test_transform, **ds_kw),
input_cls(RunningStage.PREDICTING, predict_file, transform=predict_transform, **ds_kw),
**data_module_kwargs,
)
[docs] @classmethod
def from_squad_v2(
cls,
train_file: Optional[str] = None,
val_file: Optional[str] = None,
test_file: Optional[str] = None,
predict_file: Optional[str] = None,
train_transform: INPUT_TRANSFORM_TYPE = InputTransform,
val_transform: INPUT_TRANSFORM_TYPE = InputTransform,
test_transform: INPUT_TRANSFORM_TYPE = InputTransform,
predict_transform: INPUT_TRANSFORM_TYPE = InputTransform,
input_cls: Type[Input] = QuestionAnsweringSQuADInput,
transform_kwargs: Optional[Dict] = None,
question_column_name: str = "question",
context_column_name: str = "context",
answer_column_name: str = "answer",
**data_module_kwargs: Any,
) -> "QuestionAnsweringData":
"""Load the :class:`~flash.text.question_answering.data.QuestionAnsweringData` from JSON files containing
questions, contexts and their corresponding answers in the SQuAD2.0 format.
Question snippets will be extracted from the ``question_column_name`` column in the JSON files.
Context snippets will be extracted from the ``context_column_name`` column in the JSON files.
Answer snippets will be extracted from the ``answer_column_name`` column in the JSON files.
To learn how to customize the transforms applied for each stage, read our
:ref:`customizing transforms guide <customizing_transforms>`.
Args:
train_file: The JSON file containing the training data.
val_file: The JSON file containing the validation data.
test_file: The JSON file containing the testing data.
predict_file: The JSON file containing the predict data.
train_transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use when training.
val_transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use when validating.
test_transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use when testing.
predict_transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use when
predicting.
input_cls: The :class:`~flash.core.data.io.input.Input` type to use for loading the data.
transform_kwargs: Dict of keyword arguments to be provided when instantiating the transforms.
question_column_name: The key in the JSON file to recognize the question field.
context_column_name: The key in the JSON file to recognize the context field.
answer_column_name: The key in the JSON file to recognize the answer field.
Returns:
The constructed data module.
Examples
________
.. testsetup::
>>> import json
>>> from pathlib import Path
>>> train_data = Path("train_data.json")
>>> predict_data = Path("predict_data.json")
>>> _ = train_data.write_text(
... json.dumps(
... {
... "version": "v2.0",
... "data": [
... {
... "title": "ExampleSet1",
... "paragraphs": [
... {
... "qas": [
... {
... "question": "this is a question one",
... "id": "12345",
... "answers": [{"text": "this is an answer one", "answer_start": 0}],
... "is_impossible": False,
... }
... ],
... "context": "this is an answer one. this is a context one",
... },
... {
... "qas": [
... {
... "question": "this is a question two",
... "id": "12346",
... "answers": [{"text": "this is an answer two", "answer_start": 0}],
... "is_impossible": False,
... }
... ],
... "context": "this is an answer two. this is a context two",
... },
... ],
... },
... {
... "title": "ExampleSet2",
... "paragraphs": [
... {
... "qas": [
... {
... "question": "this is a question three",
... "id": "12347",
... "answers": [{"text": "this is an answer three", "answer_start": 0}],
... "is_impossible": False,
... }
... ],
... "context": "this is an answer three. this is a context three",
... },
... {
... "qas": [
... {
... "question": "this is a question four",
... "id": "12348",
... "answers": [{"text": "this is an answer four", "answer_start": 0}],
... "is_impossible": False,
... }
... ],
... "context": "this is an answer four. this is a context four",
... },
... ],
... },
... ],
... }
... )
... )
>>> _ = predict_data.write_text(
... json.dumps(
... {
... "version": "v2.0",
... "data": [
... {
... "title": "ExampleSet3",
... "paragraphs": [
... {
... "qas": [
... {
... "question": "this is a question five",
... "id": "12349",
... "is_impossible": False,
... }
... ],
... "context": "this is an answer five. this is a context five",
... },
... {
... "qas": [
... {
... "question": "this is a question six",
... "id": "12350",
... "is_impossible": False,
... }
... ],
... "context": "this is an answer six. this is a context six",
... },
... ],
... }
... ],
... }
... )
... )
The file ``train_data.json`` contains the following:
.. code-block::
{
"version": "v2.0",
"data": [
{
"title": "ExampleSet1",
"paragraphs": [
{
"qas": [
{
"question": "this is a question one",
"id": "12345",
"answers": [{"text": "this is an answer one", "answer_start": 0}],
"is_impossible": false
}
],
"context": "this is an answer one. this is a context one"
}, {
"qas": [
{
"question": "this is a question two",
"id": "12346",
"answers": [{"text": "this is an answer two", "answer_start": 0}],
"is_impossible": false
}
],
"context": "this is an answer two. this is a context two"
}
]
}, {
"title": "ExampleSet2",
"paragraphs": [
{
"qas": [
{
"question": "this is a question three",
"id": "12347",
"answers": [{"text": "this is an answer three", "answer_start": 0}],
"is_impossible": false
}
],
"context": "this is an answer three. this is a context three"
}, {
"qas": [
{
"question": "this is a question four",
"id": "12348",
"answers": [{"text": "this is an answer four", "answer_start": 0}],
"is_impossible": false
}
],
"context": "this is an answer four. this is a context four"
}
]
}
]
}
The file ``predict_data.json`` contains the following:
.. code-block::
{
"version": "v2.0",
"data": [
{
"title": "ExampleSet3",
"paragraphs": [
{
"qas": [
{
"question": "this is a question five",
"id": "12349",
"is_impossible": false
}
],
"context": "this is an answer five. this is a context five"
}, {
"qas": [
{
"question": "this is a question six",
"id": "12350",
"is_impossible": false
}
],
"context": "this is an answer six. this is a context six"
}
]
}
]
}
.. doctest::
>>> from flash import Trainer
>>> from flash.text import QuestionAnsweringData, QuestionAnsweringTask
>>> datamodule = QuestionAnsweringData.from_squad_v2(
... train_file="train_data.json",
... predict_file="predict_data.json",
... batch_size=2,
... ) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
>>> model = QuestionAnsweringTask(max_source_length=32, max_target_length=32)
>>> 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::
>>> import os
>>> os.remove("train_data.json")
>>> os.remove("predict_data.json")
"""
ds_kw = dict(
question_column_name=question_column_name,
context_column_name=context_column_name,
answer_column_name=answer_column_name,
transform_kwargs=transform_kwargs,
input_transforms_registry=cls.input_transforms_registry,
)
return cls(
input_cls(RunningStage.TRAINING, train_file, transform=train_transform, **ds_kw),
input_cls(RunningStage.VALIDATING, val_file, transform=val_transform, **ds_kw),
input_cls(RunningStage.TESTING, test_file, transform=test_transform, **ds_kw),
input_cls(RunningStage.PREDICTING, predict_file, transform=predict_transform, **ds_kw),
**data_module_kwargs,
)
[docs] @classmethod
def from_dicts(
cls,
train_data: Optional[Dict[str, Any]] = None,
val_data: Optional[Dict[str, Any]] = None,
test_data: Optional[Dict[str, Any]] = None,
predict_data: Optional[Dict[str, Any]] = None,
train_transform: INPUT_TRANSFORM_TYPE = InputTransform,
val_transform: INPUT_TRANSFORM_TYPE = InputTransform,
test_transform: INPUT_TRANSFORM_TYPE = InputTransform,
predict_transform: INPUT_TRANSFORM_TYPE = InputTransform,
input_cls: Type[Input] = QuestionAnsweringDictionaryInput,
transform_kwargs: Optional[Dict] = None,
question_column_name: str = "question",
context_column_name: str = "context",
answer_column_name: str = "answer",
**data_module_kwargs: Any,
) -> "QuestionAnsweringData":
"""Load the :class:`~flash.text.question_answering.data.QuestionAnsweringData` from Python dictionary
objects containing questions, contexts and their corresponding answers.
Question snippets will be extracted from the ``question_column_name`` field in the dictionaries.
Context snippets will be extracted from the ``context_column_name`` field in the dictionaries.
Answer snippets will be extracted from the ``answer_column_name`` field in the dictionaries.
To learn how to customize the transforms applied for each stage, read our
:ref:`customizing transforms guide <customizing_transforms>`.
Args:
train_data: The dictionary containing the training data.
val_data: The dictionary containing the validation data.
test_data: The dictionary containing the testing data.
predict_data: The dictionary containing the data to use when predicting.
train_transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use when training.
val_transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use when validating.
test_transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use when testing.
predict_transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use when
predicting.
input_cls: The :class:`~flash.core.data.io.input.Input` type to use for loading the data.
transform_kwargs: Dict of keyword arguments to be provided when instantiating the transforms.
question_column_name: The key in the JSON file to recognize the question field.
context_column_name: The key in the JSON file to recognize the context field.
answer_column_name: The key in the JSON file to recognize the answer field.
Returns:
The constructed :class:`~flash.text.question_answering.data.QuestionAnsweringData`.
Examples
________
.. doctest::
>>> from flash import Trainer
>>> from flash.text import QuestionAnsweringData, QuestionAnsweringTask
>>> train_data = {
... "id": ["12345", "12346", "12347", "12348"],
... "context": [
... "this is an answer one. this is a context one",
... "this is an answer two. this is a context two",
... "this is an answer three. this is a context three",
... "this is an answer four. this is a context four",
... ],
... "question": [
... "this is a question one",
... "this is a question two",
... "this is a question three",
... "this is a question four",
... ],
... "answer_text": [
... "this is an answer one",
... "this is an answer two",
... "this is an answer three",
... "this is an answer four",
... ],
... "answer_start": [0, 0, 0, 0],
... }
>>> predict_data = {
... "id": ["12349", "12350"],
... "context": [
... "this is an answer five. this is a context five",
... "this is an answer six. this is a context six",
... ],
... "question": [
... "this is a question five",
... "this is a question six",
... ],
... }
>>> datamodule = QuestionAnsweringData.from_dicts(
... train_data=train_data,
... predict_data=predict_data,
... batch_size=2,
... ) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
>>> model = QuestionAnsweringTask(max_source_length=32, max_target_length=32)
>>> 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 = dict(
question_column_name=question_column_name,
context_column_name=context_column_name,
answer_column_name=answer_column_name,
transform_kwargs=transform_kwargs,
input_transforms_registry=cls.input_transforms_registry,
)
return cls(
input_cls(RunningStage.TRAINING, train_data, transform=train_transform, **ds_kw),
input_cls(RunningStage.VALIDATING, val_data, transform=val_transform, **ds_kw),
input_cls(RunningStage.TESTING, test_data, transform=test_transform, **ds_kw),
input_cls(RunningStage.PREDICTING, predict_data, transform=predict_transform, **ds_kw),
**data_module_kwargs,
)
