97 lines
4.2 KiB
Python
97 lines
4.2 KiB
Python
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# Copyright 2020 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import logging
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from dataclasses import dataclass, field
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from pathlib import Path
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from typing import Optional, Union
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from .generation.configuration_utils import GenerationConfig
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from .training_args import TrainingArguments
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from .utils import add_start_docstrings
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logger = logging.getLogger(__name__)
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@dataclass
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@add_start_docstrings(TrainingArguments.__doc__)
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class Seq2SeqTrainingArguments(TrainingArguments):
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"""
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Args:
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sortish_sampler (`bool`, *optional*, defaults to `False`):
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Whether to use a *sortish sampler* or not. Only possible if the underlying datasets are *Seq2SeqDataset*
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for now but will become generally available in the near future.
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It sorts the inputs according to lengths in order to minimize the padding size, with a bit of randomness
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for the training set.
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predict_with_generate (`bool`, *optional*, defaults to `False`):
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Whether to use generate to calculate generative metrics (ROUGE, BLEU).
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generation_max_length (`int`, *optional*):
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The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default to the
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`max_length` value of the model configuration.
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generation_num_beams (`int`, *optional*):
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The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default to the
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`num_beams` value of the model configuration.
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generation_config (`str` or `Path` or [`~generation.GenerationConfig`], *optional*):
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Allows to load a [`~generation.GenerationConfig`] from the `from_pretrained` method. This can be either:
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- a string, the *model id* of a pretrained model configuration hosted inside a model repo on
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huggingface.co.
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- a path to a *directory* containing a configuration file saved using the
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[`~GenerationConfig.save_pretrained`] method, e.g., `./my_model_directory/`.
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- a [`~generation.GenerationConfig`] object.
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"""
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sortish_sampler: bool = field(default=False, metadata={"help": "Whether to use SortishSampler or not."})
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predict_with_generate: bool = field(
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default=False, metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}
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)
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generation_max_length: Optional[int] = field(
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default=None,
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metadata={
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"help": (
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"The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
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"to the `max_length` value of the model configuration."
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)
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},
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)
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generation_num_beams: Optional[int] = field(
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default=None,
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metadata={
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"help": (
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"The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
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"to the `num_beams` value of the model configuration."
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)
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},
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)
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generation_config: Optional[Union[str, Path, GenerationConfig]] = field(
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default=None,
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metadata={
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"help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
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},
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)
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def to_dict(self):
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"""
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Serializes this instance while replace `Enum` by their values and `GenerationConfig` by dictionaries (for JSON
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serialization support). It obfuscates the token values by removing their value.
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"""
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# filter out fields that are defined as field(init=False)
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d = super().to_dict()
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for k, v in d.items():
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if isinstance(v, GenerationConfig):
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d[k] = v.to_dict()
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return d
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