File size: 2,679 Bytes
9e86264 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 |
# Copyright 2022 The HuggingFace Evaluate Authors.
#
# 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 Dict, Tuple
from datasets import Dataset
from .base import Evaluator
from .utils import DatasetColumn
TASK_DOCUMENTATION_KWARGS = r"""
input_column (`str`, defaults to `"text"`):
the name of the column containing the input text in the dataset specified by `data`.
generation_kwargs (`Dict`, *optional*, defaults to `None`):
The generation kwargs are passed to the pipeline and set the text generation strategy.
"""
class TextGenerationEvaluator(Evaluator):
"""
Text generation evaluator.
This Text generation evaluator can currently be loaded from [`evaluator`] using the default task name
`text-generation`.
Methods in this class assume a data format compatible with the [`~transformers.TextGenerationPipeline`].
"""
def predictions_processor(self, predictions, *args, **kwargs):
"""
Args:
predictions: A list of lists of dicts
Returns:
`dict`: All the generated texts are flattened and stored under the "data" key.
"""
return {"data": [pred[f"{self.predictions_prefix}_text"] for pred_list in predictions for pred in pred_list]}
def __init__(self, task="text-generation", default_metric_name=None, predictions_prefix: str = "generated"):
super().__init__(task=task, default_metric_name=default_metric_name)
self.predictions_prefix = predictions_prefix
def prepare_data(self, data: Dataset, input_column: str, *args, **kwargs) -> Tuple[Dict, DatasetColumn]:
"""
Prepare data.
Args:
data ([`Dataset`]):
Specifies the dataset we will run evaluation on.
input_column (`str`, defaults to `"text"`):
The name of the column containing the text feature in the dataset specified by `data`.
Returns:
`dict`: metric inputs.
`list`: pipeline inputs.
"""
self.check_required_columns(data, {"input_column": input_column})
return {}, DatasetColumn(data, input_column)
|