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Source code for flash.text.classification.input

# 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 functools import partial
from typing import Any, Dict, List, Optional, Union

import pandas as pd

from flash.core.data.io.classification_input import ClassificationInputMixin
from flash.core.data.io.input import DataKeys, Input
from flash.core.data.utilities.classification import MultiBinaryTargetFormatter, TargetFormatter
from flash.core.data.utilities.loading import load_data_frame
from flash.core.data.utilities.paths import PATH_TYPE
from flash.core.utilities.imports import _TEXT_AVAILABLE, requires

if _TEXT_AVAILABLE:
    from datasets import Dataset, load_dataset
else:
    Dataset = object


[docs]class TextClassificationInput(Input, ClassificationInputMixin): @staticmethod def _resolve_target(target_keys: Union[str, List[str]], element: Dict[str, Any]) -> Dict[str, Any]: if not isinstance(target_keys, List): element[DataKeys.TARGET] = element.pop(target_keys) else: element[DataKeys.TARGET] = [element[target_key] for target_key in target_keys] return element
[docs] @requires("text") def load_data( self, hf_dataset: Dataset, input_key: str, target_keys: Optional[Union[str, List[str]]] = None, target_formatter: Optional[TargetFormatter] = None, ) -> Dataset: """Loads data into HuggingFace datasets.Dataset.""" if not self.predicting: hf_dataset = hf_dataset.map(partial(self._resolve_target, target_keys)) targets = hf_dataset.to_dict()[DataKeys.TARGET] self.load_target_metadata(targets, target_formatter=target_formatter) # If we had binary multi-class targets then we also know the labels (column names) if isinstance(self.target_formatter, MultiBinaryTargetFormatter) and isinstance(target_keys, List): self.labels = target_keys # remove extra columns extra_columns = set(hf_dataset.column_names) - {input_key, DataKeys.TARGET} hf_dataset = hf_dataset.remove_columns(extra_columns) if input_key != DataKeys.INPUT: hf_dataset = hf_dataset.rename_column(input_key, DataKeys.INPUT) return hf_dataset
def load_sample(self, sample: Dict[str, Any]) -> Any: if DataKeys.TARGET in sample: sample[DataKeys.TARGET] = self.format_target(sample[DataKeys.TARGET]) return sample
[docs]class TextClassificationCSVInput(TextClassificationInput): @requires("text") def load_data( self, csv_file: PATH_TYPE, input_key: str, target_keys: Optional[Union[str, List[str]]] = None, target_formatter: Optional[TargetFormatter] = None, ) -> Dataset: return super().load_data( Dataset.from_pandas(load_data_frame(csv_file)), input_key, target_keys, target_formatter=target_formatter )
[docs]class TextClassificationJSONInput(TextClassificationInput): @requires("text") def load_data( self, json_file: PATH_TYPE, field: str, input_key: str, target_keys: Optional[Union[str, List[str]]] = None, target_formatter: Optional[TargetFormatter] = None, ) -> Dataset: dataset_dict = load_dataset("json", data_files={"data": str(json_file)}, field=field) return super().load_data(dataset_dict["data"], input_key, target_keys, target_formatter=target_formatter)
[docs]class TextClassificationDataFrameInput(TextClassificationInput): @requires("text") def load_data( self, data_frame: pd.DataFrame, input_key: str, target_keys: Optional[Union[str, List[str]]] = None, target_formatter: Optional[TargetFormatter] = None, ) -> Dataset: return super().load_data( Dataset.from_pandas(data_frame), input_key, target_keys, target_formatter=target_formatter )
[docs]class TextClassificationParquetInput(TextClassificationInput): @requires("text") def load_data( self, parquet_file: PATH_TYPE, input_key: str, target_keys: Optional[Union[str, List[str]]] = None, target_formatter: Optional[TargetFormatter] = None, ) -> Dataset: return super().load_data( Dataset.from_parquet(str(parquet_file)), input_key, target_keys, target_formatter=target_formatter )
[docs]class TextClassificationListInput(TextClassificationInput): @requires("text") def load_data( self, inputs: List[str], targets: Optional[List[Any]] = None, target_formatter: Optional[TargetFormatter] = None, ) -> Dataset: if targets is not None: hf_dataset = Dataset.from_dict({DataKeys.INPUT: inputs, DataKeys.TARGET: targets}) else: hf_dataset = Dataset.from_dict({DataKeys.INPUT: inputs}) return super().load_data(hf_dataset, DataKeys.INPUT, DataKeys.TARGET, target_formatter=target_formatter)

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