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training_args_seq2seq.py
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training_args_seq2seq.py
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# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# 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.
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
logger = logging.getLogger(__name__)
@dataclass
@add_start_docstrings(TrainingArguments.__doc__)
class Seq2SeqTrainingArguments(TrainingArguments):
"""
Args:
sortish_sampler (`bool`, *optional*, defaults to `False`):
Whether to use a *sortish sampler* or not. Only possible if the underlying datasets are *Seq2SeqDataset*
for now but will become generally available in the near future.
It sorts the inputs according to lengths in order to minimize the padding size, with a bit of randomness
for the training set.
predict_with_generate (`bool`, *optional*, defaults to `False`):
Whether to use generate to calculate generative metrics (ROUGE, BLEU).
generation_max_length (`int`, *optional*):
The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default to the
`max_length` value of the model configuration.
generation_num_beams (`int`, *optional*):
The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default to the
`num_beams` value of the model configuration.
generation_config (`str` or `Path` or [`~generation.GenerationConfig`], *optional*):
Allows to load a [`~generation.GenerationConfig`] from the `from_pretrained` method. This can be either:
- a string, the *model id* of a pretrained model configuration hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced
under a user or organization name, like `dbmdz/bert-base-german-cased`.
- a path to a *directory* containing a configuration file saved using the
[`~GenerationConfig.save_pretrained`] method, e.g., `./my_model_directory/`.
- a [`~generation.GenerationConfig`] object.
"""
sortish_sampler: bool = field(default=False, metadata={"help": "Whether to use SortishSampler or not."})
predict_with_generate: bool = field(
default=False, metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}
)
generation_max_length: Optional[int] = field(
default=None,
metadata={
"help": (
"The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
"to the `max_length` value of the model configuration."
)
},
)
generation_num_beams: Optional[int] = field(
default=None,
metadata={
"help": (
"The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
"to the `num_beams` value of the model configuration."
)
},
)
generation_config: Optional[Union[str, Path, GenerationConfig]] = field(
default=None,
metadata={
"help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
},
)
def to_dict(self):
"""
Serializes this instance while replace `Enum` by their values and `GenerationConfig` by dictionaries (for JSON
serialization support). It obfuscates the token values by removing their value.
"""
# filter out fields that are defined as field(init=False)
d = super().to_dict()
for k, v in d.items():
if isinstance(v, GenerationConfig):
d[k] = v.to_dict()
return d