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/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/processors.md | https://huggingface.co/docs/transformers/en/main_classes/processors/#processors | .md | Those processors are:
- [`~data.processors.utils.SquadV1Processor`]
- [`~data.processors.utils.SquadV2Processor`]
They both inherit from the abstract class [`~data.processors.utils.SquadProcessor`]
data.processors.squad.SquadProcessor
Processor for the SQuAD data set. overridden by SquadV1Processor and SquadV2Processor, used by the version 1.1 and
version 2.0 of SQuAD, respectively.
- all
Additionally, the following method can be used to convert SQuAD examples into | 459_7_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/processors.md | https://huggingface.co/docs/transformers/en/main_classes/processors/#processors | .md | version 2.0 of SQuAD, respectively.
- all
Additionally, the following method can be used to convert SQuAD examples into
[`~data.processors.utils.SquadFeatures`] that can be used as model inputs.
data.processors.squad.squad_convert_examples_to_features
Converts a list of examples into a list of features that can be directly given as input to a model. It is
model-dependant and takes advantage of many of the tokenizer's features to create the model's inputs.
Args: | 459_7_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/processors.md | https://huggingface.co/docs/transformers/en/main_classes/processors/#processors | .md | model-dependant and takes advantage of many of the tokenizer's features to create the model's inputs.
Args:
examples: list of [`~data.processors.squad.SquadExample`]
tokenizer: an instance of a child of [`PreTrainedTokenizer`]
max_seq_length: The maximum sequence length of the inputs.
doc_stride: The stride used when the context is too large and is split across several features.
max_query_length: The maximum length of the query. | 459_7_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/processors.md | https://huggingface.co/docs/transformers/en/main_classes/processors/#processors | .md | max_query_length: The maximum length of the query.
is_training: whether to create features for model evaluation or model training.
padding_strategy: Default to "max_length". Which padding strategy to use
return_dataset: Default False. Either 'pt' or 'tf'.
if 'pt': returns a torch.data.TensorDataset, if 'tf': returns a tf.data.Dataset
threads: multiple processing threads.
Returns:
list of [`~data.processors.squad.SquadFeatures`]
Example:
```python
processor = SquadV2Processor() | 459_7_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/processors.md | https://huggingface.co/docs/transformers/en/main_classes/processors/#processors | .md | Returns:
list of [`~data.processors.squad.SquadFeatures`]
Example:
```python
processor = SquadV2Processor()
examples = processor.get_dev_examples(data_dir) | 459_7_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/processors.md | https://huggingface.co/docs/transformers/en/main_classes/processors/#processors | .md | features = squad_convert_examples_to_features(
examples=examples,
tokenizer=tokenizer,
max_seq_length=args.max_seq_length,
doc_stride=args.doc_stride,
max_query_length=args.max_query_length,
is_training=not evaluate,
)
```
These processors as well as the aforementioned method can be used with files containing the data as well as with the
*tensorflow_datasets* package. Examples are given below. | 459_7_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/processors.md | https://huggingface.co/docs/transformers/en/main_classes/processors/#example-usage | .md | Here is an example using the processors as well as the conversion method using data files:
```python
# Loading a V2 processor
processor = SquadV2Processor()
examples = processor.get_dev_examples(squad_v2_data_dir)
# Loading a V1 processor
processor = SquadV1Processor()
examples = processor.get_dev_examples(squad_v1_data_dir) | 459_8_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/processors.md | https://huggingface.co/docs/transformers/en/main_classes/processors/#example-usage | .md | features = squad_convert_examples_to_features(
examples=examples,
tokenizer=tokenizer,
max_seq_length=max_seq_length,
doc_stride=args.doc_stride,
max_query_length=max_query_length,
is_training=not evaluate,
)
```
Using *tensorflow_datasets* is as easy as using a data file:
```python
# tensorflow_datasets only handle Squad V1.
tfds_examples = tfds.load("squad")
examples = SquadV1Processor().get_examples_from_dataset(tfds_examples, evaluate=evaluate) | 459_8_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/processors.md | https://huggingface.co/docs/transformers/en/main_classes/processors/#example-usage | .md | features = squad_convert_examples_to_features(
examples=examples,
tokenizer=tokenizer,
max_seq_length=max_seq_length,
doc_stride=args.doc_stride,
max_query_length=max_query_length,
is_training=not evaluate,
)
```
Another example using these processors is given in the [run_squad.py](https://github.com/huggingface/transformers/tree/main/examples/legacy/question-answering/run_squad.py) script. | 459_8_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/ | .md | <!--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 | 460_0_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/ | .md | 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.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
--> | 460_0_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainer | .md | The [`Trainer`] class provides an API for feature-complete training in PyTorch, and it supports distributed training on multiple GPUs/TPUs, mixed precision for [NVIDIA GPUs](https://nvidia.github.io/apex/), [AMD GPUs](https://rocm.docs.amd.com/en/latest/rocm.html), and [`torch.amp`](https://pytorch.org/docs/stable/amp.html) for PyTorch. [`Trainer`] goes hand-in-hand with the [`TrainingArguments`] class, which offers a wide range of options to customize how a model is trained. Together, these two classes | 460_1_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainer | .md | class, which offers a wide range of options to customize how a model is trained. Together, these two classes provide a complete training API. | 460_1_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainer | .md | [`Seq2SeqTrainer`] and [`Seq2SeqTrainingArguments`] inherit from the [`Trainer`] and [`TrainingArguments`] classes and they're adapted for training models for sequence-to-sequence tasks such as summarization or translation.
<Tip warning={true}>
The [`Trainer`] class is optimized for 🤗 Transformers models and can have surprising behaviors
when used with other models. When using it with your own model, make sure:
- your model always return tuples or subclasses of [`~utils.ModelOutput`] | 460_1_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainer | .md | - your model always return tuples or subclasses of [`~utils.ModelOutput`]
- your model can compute the loss if a `labels` argument is provided and that loss is returned as the first
element of the tuple (if your model returns tuples)
- your model can accept multiple label arguments (use `label_names` in [`TrainingArguments`] to indicate their name to the [`Trainer`]) but none of them should be named `"label"`
</Tip> | 460_1_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainerapi-reference | .md | Trainer is a simple but feature-complete training and eval loop for PyTorch, optimized for 🤗 Transformers.
Args:
model ([`PreTrainedModel`] or `torch.nn.Module`, *optional*):
The model to train, evaluate or use for predictions. If not provided, a `model_init` must be passed.
<Tip>
[`Trainer`] is optimized to work with the [`PreTrainedModel`] provided by the library. You can still use
your own models defined as `torch.nn.Module` as long as they work the same way as the 🤗 Transformers
models.
</Tip> | 460_2_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainerapi-reference | .md | your own models defined as `torch.nn.Module` as long as they work the same way as the 🤗 Transformers
models.
</Tip>
args ([`TrainingArguments`], *optional*):
The arguments to tweak for training. Will default to a basic instance of [`TrainingArguments`] with the
`output_dir` set to a directory named *tmp_trainer* in the current directory if not provided.
data_collator (`DataCollator`, *optional*):
The function to use to form a batch from a list of elements of `train_dataset` or `eval_dataset`. Will | 460_2_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainerapi-reference | .md | The function to use to form a batch from a list of elements of `train_dataset` or `eval_dataset`. Will
default to [`default_data_collator`] if no `processing_class` is provided, an instance of
[`DataCollatorWithPadding`] otherwise if the processing_class is a feature extractor or tokenizer.
train_dataset (Union[`torch.utils.data.Dataset`, `torch.utils.data.IterableDataset`, `datasets.Dataset`], *optional*):
The dataset to use for training. If it is a [`~datasets.Dataset`], columns not accepted by the | 460_2_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainerapi-reference | .md | The dataset to use for training. If it is a [`~datasets.Dataset`], columns not accepted by the
`model.forward()` method are automatically removed.
Note that if it's a `torch.utils.data.IterableDataset` with some randomization and you are training in a
distributed fashion, your iterable dataset should either use a internal attribute `generator` that is a
`torch.Generator` for the randomization that must be identical on all processes (and the Trainer will | 460_2_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainerapi-reference | .md | `torch.Generator` for the randomization that must be identical on all processes (and the Trainer will
manually set the seed of this `generator` at each epoch) or have a `set_epoch()` method that internally
sets the seed of the RNGs used.
eval_dataset (Union[`torch.utils.data.Dataset`, Dict[str, `torch.utils.data.Dataset`, `datasets.Dataset`]), *optional*):
The dataset to use for evaluation. If it is a [`~datasets.Dataset`], columns not accepted by the | 460_2_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainerapi-reference | .md | The dataset to use for evaluation. If it is a [`~datasets.Dataset`], columns not accepted by the
`model.forward()` method are automatically removed. If it is a dictionary, it will evaluate on each
dataset prepending the dictionary key to the metric name.
processing_class (`PreTrainedTokenizerBase` or `BaseImageProcessor` or `FeatureExtractionMixin` or `ProcessorMixin`, *optional*):
Processing class used to process the data. If provided, will be used to automatically process the inputs | 460_2_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainerapi-reference | .md | Processing class used to process the data. If provided, will be used to automatically process the inputs
for the model, and it will be saved along the model to make it easier to rerun an interrupted training or
reuse the fine-tuned model.
This supercedes the `tokenizer` argument, which is now deprecated.
model_init (`Callable[[], PreTrainedModel]`, *optional*):
A function that instantiates the model to be used. If provided, each call to [`~Trainer.train`] will start | 460_2_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainerapi-reference | .md | A function that instantiates the model to be used. If provided, each call to [`~Trainer.train`] will start
from a new instance of the model as given by this function.
The function may have zero argument, or a single one containing the optuna/Ray Tune/SigOpt trial object, to
be able to choose different architectures according to hyper parameters (such as layer count, sizes of
inner layers, dropout probabilities etc).
compute_loss_func (`Callable`, *optional*): | 460_2_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainerapi-reference | .md | inner layers, dropout probabilities etc).
compute_loss_func (`Callable`, *optional*):
A function that accepts the raw model outputs, labels, and the number of items in the entire accumulated
batch (batch_size * gradient_accumulation_steps) and returns the loss. For example, see the default [loss function](https://github.com/huggingface/transformers/blob/052e652d6d53c2b26ffde87e039b723949a53493/src/transformers/trainer.py#L3618) used by [`Trainer`]. | 460_2_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainerapi-reference | .md | compute_metrics (`Callable[[EvalPrediction], Dict]`, *optional*):
The function that will be used to compute metrics at evaluation. Must take a [`EvalPrediction`] and return
a dictionary string to metric values. *Note* When passing TrainingArgs with `batch_eval_metrics` set to
`True`, your compute_metrics function must take a boolean `compute_result` argument. This will be triggered
after the last eval batch to signal that the function needs to calculate and return the global summary | 460_2_9 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainerapi-reference | .md | after the last eval batch to signal that the function needs to calculate and return the global summary
statistics rather than accumulating the batch-level statistics
callbacks (List of [`TrainerCallback`], *optional*):
A list of callbacks to customize the training loop. Will add those to the list of default callbacks
detailed in [here](callback).
If you want to remove one of the default callbacks used, use the [`Trainer.remove_callback`] method. | 460_2_10 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainerapi-reference | .md | If you want to remove one of the default callbacks used, use the [`Trainer.remove_callback`] method.
optimizers (`Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`, *optional*, defaults to `(None, None)`):
A tuple containing the optimizer and the scheduler to use. Will default to an instance of [`AdamW`] on your
model and a scheduler given by [`get_linear_schedule_with_warmup`] controlled by `args`.
optimizer_cls_and_kwargs (`Tuple[Type[torch.optim.Optimizer], Dict[str, Any]]`, *optional*): | 460_2_11 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainerapi-reference | .md | optimizer_cls_and_kwargs (`Tuple[Type[torch.optim.Optimizer], Dict[str, Any]]`, *optional*):
A tuple containing the optimizer class and keyword arguments to use.
Overrides `optim` and `optim_args` in `args`. Incompatible with the `optimizers` argument.
Unlike `optimizers`, this argument avoids the need to place model parameters on the correct devices before initializing the Trainer.
preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`, *optional*): | 460_2_12 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainerapi-reference | .md | preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`, *optional*):
A function that preprocess the logits right before caching them at each evaluation step. Must take two
tensors, the logits and the labels, and return the logits once processed as desired. The modifications made
by this function will be reflected in the predictions received by `compute_metrics`.
Note that the labels (second parameter) will be `None` if the dataset does not have them. | 460_2_13 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainerapi-reference | .md | Note that the labels (second parameter) will be `None` if the dataset does not have them.
Important attributes:
- **model** -- Always points to the core model. If using a transformers model, it will be a [`PreTrainedModel`]
subclass.
- **model_wrapped** -- Always points to the most external model in case one or more other modules wrap the
original model. This is the model that should be used for the forward pass. For example, under `DeepSpeed`, | 460_2_14 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainerapi-reference | .md | original model. This is the model that should be used for the forward pass. For example, under `DeepSpeed`,
the inner model is wrapped in `DeepSpeed` and then again in `torch.nn.DistributedDataParallel`. If the inner
model hasn't been wrapped, then `self.model_wrapped` is the same as `self.model`.
- **is_model_parallel** -- Whether or not a model has been switched to a model parallel mode (different from
data parallelism, this means some of the model layers are split on different GPUs). | 460_2_15 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainerapi-reference | .md | data parallelism, this means some of the model layers are split on different GPUs).
- **place_model_on_device** -- Whether or not to automatically place the model on the device - it will be set
to `False` if model parallel or deepspeed is used, or if the default
`TrainingArguments.place_model_on_device` is overridden to return `False` .
- **is_in_train** -- Whether or not a model is currently running `train` (e.g. when `evaluate` is called while
in `train`)
- all | 460_2_16 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#seq2seqtrainer | .md | Seq2SeqTrainer
- evaluate
- predict | 460_3_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | TrainingArguments is the subset of the arguments we use in our example scripts **which relate to the training loop
itself**.
Using [`HfArgumentParser`] we can turn this class into
[argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the
command line.
Parameters:
output_dir (`str`):
The output directory where the model predictions and checkpoints will be written.
overwrite_output_dir (`bool`, *optional*, defaults to `False`): | 460_4_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | overwrite_output_dir (`bool`, *optional*, defaults to `False`):
If `True`, overwrite the content of the output directory. Use this to continue training if `output_dir`
points to a checkpoint directory.
do_train (`bool`, *optional*, defaults to `False`):
Whether to run training or not. This argument is not directly used by [`Trainer`], it's intended to be used
by your training/evaluation scripts instead. See the [example | 460_4_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | by your training/evaluation scripts instead. See the [example
scripts](https://github.com/huggingface/transformers/tree/main/examples) for more details.
do_eval (`bool`, *optional*):
Whether to run evaluation on the validation set or not. Will be set to `True` if `eval_strategy` is
different from `"no"`. This argument is not directly used by [`Trainer`], it's intended to be used by your
training/evaluation scripts instead. See the [example | 460_4_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | training/evaluation scripts instead. See the [example
scripts](https://github.com/huggingface/transformers/tree/main/examples) for more details.
do_predict (`bool`, *optional*, defaults to `False`):
Whether to run predictions on the test set or not. This argument is not directly used by [`Trainer`], it's
intended to be used by your training/evaluation scripts instead. See the [example
scripts](https://github.com/huggingface/transformers/tree/main/examples) for more details. | 460_4_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | scripts](https://github.com/huggingface/transformers/tree/main/examples) for more details.
eval_strategy (`str` or [`~trainer_utils.IntervalStrategy`], *optional*, defaults to `"no"`):
The evaluation strategy to adopt during training. Possible values are:
- `"no"`: No evaluation is done during training.
- `"steps"`: Evaluation is done (and logged) every `eval_steps`.
- `"epoch"`: Evaluation is done at the end of each epoch.
prediction_loss_only (`bool`, *optional*, defaults to `False`): | 460_4_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | - `"epoch"`: Evaluation is done at the end of each epoch.
prediction_loss_only (`bool`, *optional*, defaults to `False`):
When performing evaluation and generating predictions, only returns the loss.
per_device_train_batch_size (`int`, *optional*, defaults to 8):
The batch size per GPU/XPU/TPU/MPS/NPU core/CPU for training.
per_device_eval_batch_size (`int`, *optional*, defaults to 8):
The batch size per GPU/XPU/TPU/MPS/NPU core/CPU for evaluation. | 460_4_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | per_device_eval_batch_size (`int`, *optional*, defaults to 8):
The batch size per GPU/XPU/TPU/MPS/NPU core/CPU for evaluation.
gradient_accumulation_steps (`int`, *optional*, defaults to 1):
Number of updates steps to accumulate the gradients for, before performing a backward/update pass.
<Tip warning={true}>
When using gradient accumulation, one step is counted as one step with backward pass. Therefore, logging, | 460_4_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | When using gradient accumulation, one step is counted as one step with backward pass. Therefore, logging,
evaluation, save will be conducted every `gradient_accumulation_steps * xxx_step` training examples.
</Tip>
eval_accumulation_steps (`int`, *optional*):
Number of predictions steps to accumulate the output tensors for, before moving the results to the CPU. If
left unset, the whole predictions are accumulated on GPU/NPU/TPU before being moved to the CPU (faster but
requires more memory). | 460_4_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | requires more memory).
eval_delay (`float`, *optional*):
Number of epochs or steps to wait for before the first evaluation can be performed, depending on the
eval_strategy.
torch_empty_cache_steps (`int`, *optional*):
Number of steps to wait before calling `torch.<device>.empty_cache()`. If left unset or set to None, cache will not be emptied.
<Tip> | 460_4_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | <Tip>
This can help avoid CUDA out-of-memory errors by lowering peak VRAM usage at a cost of about [10% slower performance](https://github.com/huggingface/transformers/issues/31372).
</Tip>
learning_rate (`float`, *optional*, defaults to 5e-5):
The initial learning rate for [`AdamW`] optimizer.
weight_decay (`float`, *optional*, defaults to 0):
The weight decay to apply (if not zero) to all layers except all bias and LayerNorm weights in [`AdamW`]
optimizer. | 460_4_9 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | The weight decay to apply (if not zero) to all layers except all bias and LayerNorm weights in [`AdamW`]
optimizer.
adam_beta1 (`float`, *optional*, defaults to 0.9):
The beta1 hyperparameter for the [`AdamW`] optimizer.
adam_beta2 (`float`, *optional*, defaults to 0.999):
The beta2 hyperparameter for the [`AdamW`] optimizer.
adam_epsilon (`float`, *optional*, defaults to 1e-8):
The epsilon hyperparameter for the [`AdamW`] optimizer.
max_grad_norm (`float`, *optional*, defaults to 1.0): | 460_4_10 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | The epsilon hyperparameter for the [`AdamW`] optimizer.
max_grad_norm (`float`, *optional*, defaults to 1.0):
Maximum gradient norm (for gradient clipping).
num_train_epochs(`float`, *optional*, defaults to 3.0):
Total number of training epochs to perform (if not an integer, will perform the decimal part percents of
the last epoch before stopping training).
max_steps (`int`, *optional*, defaults to -1): | 460_4_11 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | the last epoch before stopping training).
max_steps (`int`, *optional*, defaults to -1):
If set to a positive number, the total number of training steps to perform. Overrides `num_train_epochs`.
For a finite dataset, training is reiterated through the dataset (if all data is exhausted) until
`max_steps` is reached.
lr_scheduler_type (`str` or [`SchedulerType`], *optional*, defaults to `"linear"`):
The scheduler type to use. See the documentation of [`SchedulerType`] for all possible values. | 460_4_12 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | The scheduler type to use. See the documentation of [`SchedulerType`] for all possible values.
lr_scheduler_kwargs ('dict', *optional*, defaults to {}):
The extra arguments for the lr_scheduler. See the documentation of each scheduler for possible values.
warmup_ratio (`float`, *optional*, defaults to 0.0):
Ratio of total training steps used for a linear warmup from 0 to `learning_rate`.
warmup_steps (`int`, *optional*, defaults to 0): | 460_4_13 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | warmup_steps (`int`, *optional*, defaults to 0):
Number of steps used for a linear warmup from 0 to `learning_rate`. Overrides any effect of `warmup_ratio`.
log_level (`str`, *optional*, defaults to `passive`):
Logger log level to use on the main process. Possible choices are the log levels as strings: 'debug',
'info', 'warning', 'error' and 'critical', plus a 'passive' level which doesn't set anything and keeps the
current log level for the Transformers library (which will be `"warning"` by default). | 460_4_14 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | current log level for the Transformers library (which will be `"warning"` by default).
log_level_replica (`str`, *optional*, defaults to `"warning"`):
Logger log level to use on replicas. Same choices as `log_level`"
log_on_each_node (`bool`, *optional*, defaults to `True`):
In multinode distributed training, whether to log using `log_level` once per node, or only on the main
node.
logging_dir (`str`, *optional*):
[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to | 460_4_15 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | node.
logging_dir (`str`, *optional*):
[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to
*output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.
logging_strategy (`str` or [`~trainer_utils.IntervalStrategy`], *optional*, defaults to `"steps"`):
The logging strategy to adopt during training. Possible values are:
- `"no"`: No logging is done during training.
- `"epoch"`: Logging is done at the end of each epoch.
- `"steps"`: Logging is done every `logging_steps`. | 460_4_16 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | - `"epoch"`: Logging is done at the end of each epoch.
- `"steps"`: Logging is done every `logging_steps`.
logging_first_step (`bool`, *optional*, defaults to `False`):
Whether to log the first `global_step` or not.
logging_steps (`int` or `float`, *optional*, defaults to 500):
Number of update steps between two logs if `logging_strategy="steps"`. Should be an integer or a float in
range `[0,1)`. If smaller than 1, will be interpreted as ratio of total training steps. | 460_4_17 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | range `[0,1)`. If smaller than 1, will be interpreted as ratio of total training steps.
logging_nan_inf_filter (`bool`, *optional*, defaults to `True`):
Whether to filter `nan` and `inf` losses for logging. If set to `True` the loss of every step that is `nan`
or `inf` is filtered and the average loss of the current logging window is taken instead.
<Tip>
`logging_nan_inf_filter` only influences the logging of loss values, it does not change the behavior the | 460_4_18 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | <Tip>
`logging_nan_inf_filter` only influences the logging of loss values, it does not change the behavior the
gradient is computed or applied to the model.
</Tip>
save_strategy (`str` or [`~trainer_utils.SaveStrategy`], *optional*, defaults to `"steps"`):
The checkpoint save strategy to adopt during training. Possible values are:
- `"no"`: No save is done during training.
- `"epoch"`: Save is done at the end of each epoch.
- `"steps"`: Save is done every `save_steps`. | 460_4_19 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | - `"epoch"`: Save is done at the end of each epoch.
- `"steps"`: Save is done every `save_steps`.
- `"best"`: Save is done whenever a new `best_metric` is achieved.
If `"epoch"` or `"steps"` is chosen, saving will also be performed at the
very end of training, always.
save_steps (`int` or `float`, *optional*, defaults to 500):
Number of updates steps before two checkpoint saves if `save_strategy="steps"`. Should be an integer or a | 460_4_20 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | Number of updates steps before two checkpoint saves if `save_strategy="steps"`. Should be an integer or a
float in range `[0,1)`. If smaller than 1, will be interpreted as ratio of total training steps.
save_total_limit (`int`, *optional*):
If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in
`output_dir`. When `load_best_model_at_end` is enabled, the "best" checkpoint according to | 460_4_21 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | `output_dir`. When `load_best_model_at_end` is enabled, the "best" checkpoint according to
`metric_for_best_model` will always be retained in addition to the most recent ones. For example, for
`save_total_limit=5` and `load_best_model_at_end`, the four last checkpoints will always be retained
alongside the best model. When `save_total_limit=1` and `load_best_model_at_end`, it is possible that two
checkpoints are saved: the last one and the best one (if they are different). | 460_4_22 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | checkpoints are saved: the last one and the best one (if they are different).
save_safetensors (`bool`, *optional*, defaults to `True`):
Use [safetensors](https://huggingface.co/docs/safetensors) saving and loading for state dicts instead of
default `torch.load` and `torch.save`.
save_on_each_node (`bool`, *optional*, defaults to `False`):
When doing multi-node distributed training, whether to save models and checkpoints on each node, or only on
the main one. | 460_4_23 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | When doing multi-node distributed training, whether to save models and checkpoints on each node, or only on
the main one.
This should not be activated when the different nodes use the same storage as the files will be saved with
the same names for each node.
save_only_model (`bool`, *optional*, defaults to `False`):
When checkpointing, whether to only save the model, or also the optimizer, scheduler & rng state.
Note that when this is true, you won't be able to resume training from checkpoint. | 460_4_24 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | Note that when this is true, you won't be able to resume training from checkpoint.
This enables you to save storage by not storing the optimizer, scheduler & rng state.
You can only load the model using `from_pretrained` with this option set to `True`.
restore_callback_states_from_checkpoint (`bool`, *optional*, defaults to `False`):
Whether to restore the callback states from the checkpoint. If `True`, will override
callbacks passed to the `Trainer` if they exist in the checkpoint." | 460_4_25 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | callbacks passed to the `Trainer` if they exist in the checkpoint."
use_cpu (`bool`, *optional*, defaults to `False`):
Whether or not to use cpu. If set to False, we will use cuda or mps device if available.
seed (`int`, *optional*, defaults to 42):
Random seed that will be set at the beginning of training. To ensure reproducibility across runs, use the
[`~Trainer.model_init`] function to instantiate the model if it has some randomly initialized parameters.
data_seed (`int`, *optional*): | 460_4_26 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | data_seed (`int`, *optional*):
Random seed to be used with data samplers. If not set, random generators for data sampling will use the
same seed as `seed`. This can be used to ensure reproducibility of data sampling, independent of the model
seed.
jit_mode_eval (`bool`, *optional*, defaults to `False`):
Whether or not to use PyTorch jit trace for inference.
use_ipex (`bool`, *optional*, defaults to `False`):
Use Intel extension for PyTorch when it is available. [IPEX | 460_4_27 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | use_ipex (`bool`, *optional*, defaults to `False`):
Use Intel extension for PyTorch when it is available. [IPEX
installation](https://github.com/intel/intel-extension-for-pytorch).
bf16 (`bool`, *optional*, defaults to `False`):
Whether to use bf16 16-bit (mixed) precision training instead of 32-bit training. Requires Ampere or higher
NVIDIA architecture or using CPU (use_cpu) or Ascend NPU. This is an experimental API and it may change.
fp16 (`bool`, *optional*, defaults to `False`): | 460_4_28 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | fp16 (`bool`, *optional*, defaults to `False`):
Whether to use fp16 16-bit (mixed) precision training instead of 32-bit training.
fp16_opt_level (`str`, *optional*, defaults to 'O1'):
For `fp16` training, Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. See details on
the [Apex documentation](https://nvidia.github.io/apex/amp).
fp16_backend (`str`, *optional*, defaults to `"auto"`):
This argument is deprecated. Use `half_precision_backend` instead. | 460_4_29 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | fp16_backend (`str`, *optional*, defaults to `"auto"`):
This argument is deprecated. Use `half_precision_backend` instead.
half_precision_backend (`str`, *optional*, defaults to `"auto"`):
The backend to use for mixed precision training. Must be one of `"auto", "apex", "cpu_amp"`. `"auto"` will
use CPU/CUDA AMP or APEX depending on the PyTorch version detected, while the other choices will force the
requested backend.
bf16_full_eval (`bool`, *optional*, defaults to `False`): | 460_4_30 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | requested backend.
bf16_full_eval (`bool`, *optional*, defaults to `False`):
Whether to use full bfloat16 evaluation instead of 32-bit. This will be faster and save memory but can harm
metric values. This is an experimental API and it may change.
fp16_full_eval (`bool`, *optional*, defaults to `False`):
Whether to use full float16 evaluation instead of 32-bit. This will be faster and save memory but can harm
metric values.
tf32 (`bool`, *optional*): | 460_4_31 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | metric values.
tf32 (`bool`, *optional*):
Whether to enable the TF32 mode, available in Ampere and newer GPU architectures. The default value depends
on PyTorch's version default of `torch.backends.cuda.matmul.allow_tf32`. For more details please refer to
the [TF32](https://huggingface.co/docs/transformers/perf_train_gpu_one#tf32) documentation. This is an
experimental API and it may change.
local_rank (`int`, *optional*, defaults to -1):
Rank of the process during distributed training. | 460_4_32 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | local_rank (`int`, *optional*, defaults to -1):
Rank of the process during distributed training.
ddp_backend (`str`, *optional*):
The backend to use for distributed training. Must be one of `"nccl"`, `"mpi"`, `"ccl"`, `"gloo"`, `"hccl"`.
tpu_num_cores (`int`, *optional*):
When training on TPU, the number of TPU cores (automatically passed by launcher script).
dataloader_drop_last (`bool`, *optional*, defaults to `False`): | 460_4_33 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | dataloader_drop_last (`bool`, *optional*, defaults to `False`):
Whether to drop the last incomplete batch (if the length of the dataset is not divisible by the batch size)
or not.
eval_steps (`int` or `float`, *optional*):
Number of update steps between two evaluations if `eval_strategy="steps"`. Will default to the same
value as `logging_steps` if not set. Should be an integer or a float in range `[0,1)`. If smaller than 1,
will be interpreted as ratio of total training steps. | 460_4_34 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | will be interpreted as ratio of total training steps.
dataloader_num_workers (`int`, *optional*, defaults to 0):
Number of subprocesses to use for data loading (PyTorch only). 0 means that the data will be loaded in the
main process.
past_index (`int`, *optional*, defaults to -1):
Some models like [TransformerXL](../model_doc/transformerxl) or [XLNet](../model_doc/xlnet) can make use of
the past hidden states for their predictions. If this argument is set to a positive int, the `Trainer` will | 460_4_35 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | the past hidden states for their predictions. If this argument is set to a positive int, the `Trainer` will
use the corresponding output (usually index 2) as the past state and feed it to the model at the next
training step under the keyword argument `mems`.
run_name (`str`, *optional*, defaults to `output_dir`):
A descriptor for the run. Typically used for [wandb](https://www.wandb.com/),
[mlflow](https://www.mlflow.org/) and [comet](https://www.comet.com/site) logging. If not specified, will | 460_4_36 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | [mlflow](https://www.mlflow.org/) and [comet](https://www.comet.com/site) logging. If not specified, will
be the same as `output_dir`.
disable_tqdm (`bool`, *optional*):
Whether or not to disable the tqdm progress bars and table of metrics produced by
[`~notebook.NotebookTrainingTracker`] in Jupyter Notebooks. Will default to `True` if the logging level is
set to warn or lower (default), `False` otherwise.
remove_unused_columns (`bool`, *optional*, defaults to `True`): | 460_4_37 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | set to warn or lower (default), `False` otherwise.
remove_unused_columns (`bool`, *optional*, defaults to `True`):
Whether or not to automatically remove the columns unused by the model forward method.
label_names (`List[str]`, *optional*):
The list of keys in your dictionary of inputs that correspond to the labels.
Will eventually default to the list of argument names accepted by the model that contain the word "label", | 460_4_38 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | Will eventually default to the list of argument names accepted by the model that contain the word "label",
except if the model used is one of the `XxxForQuestionAnswering` in which case it will also include the
`["start_positions", "end_positions"]` keys.
load_best_model_at_end (`bool`, *optional*, defaults to `False`):
Whether or not to load the best model found during training at the end of training. When this option is
enabled, the best checkpoint will always be saved. See | 460_4_39 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | enabled, the best checkpoint will always be saved. See
[`save_total_limit`](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.TrainingArguments.save_total_limit)
for more.
<Tip>
When set to `True`, the parameters `save_strategy` needs to be the same as `eval_strategy`, and in
the case it is "steps", `save_steps` must be a round multiple of `eval_steps`.
</Tip>
metric_for_best_model (`str`, *optional*): | 460_4_40 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | </Tip>
metric_for_best_model (`str`, *optional*):
Use in conjunction with `load_best_model_at_end` to specify the metric to use to compare two different
models. Must be the name of a metric returned by the evaluation with or without the prefix `"eval_"`.
If not specified, this will default to `"loss"` when either `load_best_model_at_end == True`
or `lr_scheduler_type == SchedulerType.REDUCE_ON_PLATEAU` (to use the evaluation loss). | 460_4_41 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | or `lr_scheduler_type == SchedulerType.REDUCE_ON_PLATEAU` (to use the evaluation loss).
If you set this value, `greater_is_better` will default to `True` unless the name ends with "loss".
Don't forget to set it to `False` if your metric is better when lower.
greater_is_better (`bool`, *optional*):
Use in conjunction with `load_best_model_at_end` and `metric_for_best_model` to specify if better models
should have a greater metric or not. Will default to: | 460_4_42 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | should have a greater metric or not. Will default to:
- `True` if `metric_for_best_model` is set to a value that doesn't end in `"loss"`.
- `False` if `metric_for_best_model` is not set, or set to a value that ends in `"loss"`.
ignore_data_skip (`bool`, *optional*, defaults to `False`):
When resuming training, whether or not to skip the epochs and batches to get the data loading at the same
stage as in the previous training. If set to `True`, the training will begin faster (as that skipping step | 460_4_43 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | stage as in the previous training. If set to `True`, the training will begin faster (as that skipping step
can take a long time) but will not yield the same results as the interrupted training would have.
fsdp (`bool`, `str` or list of [`~trainer_utils.FSDPOption`], *optional*, defaults to `''`):
Use PyTorch Distributed Parallel Training (in distributed training only).
A list of options along the following:
- `"full_shard"`: Shard parameters, gradients and optimizer states. | 460_4_44 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | A list of options along the following:
- `"full_shard"`: Shard parameters, gradients and optimizer states.
- `"shard_grad_op"`: Shard optimizer states and gradients.
- `"hybrid_shard"`: Apply `FULL_SHARD` within a node, and replicate parameters across nodes.
- `"hybrid_shard_zero2"`: Apply `SHARD_GRAD_OP` within a node, and replicate parameters across nodes.
- `"offload"`: Offload parameters and gradients to CPUs (only compatible with `"full_shard"` and
`"shard_grad_op"`). | 460_4_45 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | - `"offload"`: Offload parameters and gradients to CPUs (only compatible with `"full_shard"` and
`"shard_grad_op"`).
- `"auto_wrap"`: Automatically recursively wrap layers with FSDP using `default_auto_wrap_policy`.
fsdp_config (`str` or `dict`, *optional*):
Config to be used with fsdp (Pytorch Distributed Parallel Training). The value is either a location of
fsdp json config file (e.g., `fsdp_config.json`) or an already loaded json file as `dict`.
A List of config and its options: | 460_4_46 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | fsdp json config file (e.g., `fsdp_config.json`) or an already loaded json file as `dict`.
A List of config and its options:
- min_num_params (`int`, *optional*, defaults to `0`):
FSDP's minimum number of parameters for Default Auto Wrapping. (useful only when `fsdp` field is
passed).
- transformer_layer_cls_to_wrap (`List[str]`, *optional*):
List of transformer layer class names (case-sensitive) to wrap, e.g, `BertLayer`, `GPTJBlock`,
`T5Block` .... (useful only when `fsdp` flag is passed). | 460_4_47 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | `T5Block` .... (useful only when `fsdp` flag is passed).
- backward_prefetch (`str`, *optional*)
FSDP's backward prefetch mode. Controls when to prefetch next set of parameters (useful only when
`fsdp` field is passed).
A list of options along the following:
- `"backward_pre"` : Prefetches the next set of parameters before the current set of parameter's
gradient
computation.
- `"backward_post"` : This prefetches the next set of parameters after the current set of
parameter’s
gradient computation. | 460_4_48 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | - `"backward_post"` : This prefetches the next set of parameters after the current set of
parameter’s
gradient computation.
- forward_prefetch (`bool`, *optional*, defaults to `False`)
FSDP's forward prefetch mode (useful only when `fsdp` field is passed).
If `"True"`, then FSDP explicitly prefetches the next upcoming all-gather while executing in the
forward pass.
- limit_all_gathers (`bool`, *optional*, defaults to `False`)
FSDP's limit_all_gathers (useful only when `fsdp` field is passed). | 460_4_49 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | FSDP's limit_all_gathers (useful only when `fsdp` field is passed).
If `"True"`, FSDP explicitly synchronizes the CPU thread to prevent too many in-flight
all-gathers.
- use_orig_params (`bool`, *optional*, defaults to `True`)
If `"True"`, allows non-uniform `requires_grad` during init, which means support for interspersed
frozen and trainable paramteres. Useful in cases such as parameter-efficient fine-tuning. Please
refer this | 460_4_50 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | frozen and trainable paramteres. Useful in cases such as parameter-efficient fine-tuning. Please
refer this
[blog](https://dev-discuss.pytorch.org/t/rethinking-pytorch-fully-sharded-data-parallel-fsdp-from-first-principles/1019
- sync_module_states (`bool`, *optional*, defaults to `True`)
If `"True"`, each individually wrapped FSDP unit will broadcast module parameters from rank 0 to
ensure they are the same across all ranks after initialization | 460_4_51 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | ensure they are the same across all ranks after initialization
- cpu_ram_efficient_loading (`bool`, *optional*, defaults to `False`)
If `"True"`, only the first process loads the pretrained model checkpoint while all other processes
have empty weights. When this setting as `"True"`, `sync_module_states` also must to be `"True"`,
otherwise all the processes except the main process would have random weights leading to unexpected
behaviour during training. | 460_4_52 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | otherwise all the processes except the main process would have random weights leading to unexpected
behaviour during training.
- activation_checkpointing (`bool`, *optional*, defaults to `False`):
If `"True"`, activation checkpointing is a technique to reduce memory usage by clearing activations of
certain layers and recomputing them during a backward pass. Effectively, this trades extra
computation time for reduced memory usage.
- xla (`bool`, *optional*, defaults to `False`): | 460_4_53 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | computation time for reduced memory usage.
- xla (`bool`, *optional*, defaults to `False`):
Whether to use PyTorch/XLA Fully Sharded Data Parallel Training. This is an experimental feature
and its API may evolve in the future.
- xla_fsdp_settings (`dict`, *optional*)
The value is a dictionary which stores the XLA FSDP wrapping parameters.
For a complete list of options, please see [here](
https://github.com/pytorch/xla/blob/master/torch_xla/distributed/fsdp/xla_fully_sharded_data_parallel.py). | 460_4_54 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | https://github.com/pytorch/xla/blob/master/torch_xla/distributed/fsdp/xla_fully_sharded_data_parallel.py).
- xla_fsdp_grad_ckpt (`bool`, *optional*, defaults to `False`):
Will use gradient checkpointing over each nested XLA FSDP wrapped layer. This setting can only be
used when the xla flag is set to true, and an auto wrapping policy is specified through
fsdp_min_num_params or fsdp_transformer_layer_cls_to_wrap.
deepspeed (`str` or `dict`, *optional*): | 460_4_55 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | fsdp_min_num_params or fsdp_transformer_layer_cls_to_wrap.
deepspeed (`str` or `dict`, *optional*):
Use [Deepspeed](https://github.com/microsoft/deepspeed). This is an experimental feature and its API may
evolve in the future. The value is either the location of DeepSpeed json config file (e.g.,
`ds_config.json`) or an already loaded json file as a `dict`"
<Tip warning={true}>
If enabling any Zero-init, make sure that your model is not initialized until | 460_4_56 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | <Tip warning={true}>
If enabling any Zero-init, make sure that your model is not initialized until
*after* initializing the `TrainingArguments`, else it will not be applied.
</Tip>
accelerator_config (`str`, `dict`, or `AcceleratorConfig`, *optional*):
Config to be used with the internal `Accelerator` implementation. The value is either a location of
accelerator json config file (e.g., `accelerator_config.json`), an already loaded json file as `dict`, | 460_4_57 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | accelerator json config file (e.g., `accelerator_config.json`), an already loaded json file as `dict`,
or an instance of [`~trainer_pt_utils.AcceleratorConfig`].
A list of config and its options:
- split_batches (`bool`, *optional*, defaults to `False`):
Whether or not the accelerator should split the batches yielded by the dataloaders across the devices. If
`True` the actual batch size used will be the same on any kind of distributed processes, but it must be a | 460_4_58 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | `True` the actual batch size used will be the same on any kind of distributed processes, but it must be a
round multiple of the `num_processes` you are using. If `False`, actual batch size used will be the one set
in your script multiplied by the number of processes.
- dispatch_batches (`bool`, *optional*):
If set to `True`, the dataloader prepared by the Accelerator is only iterated through on the main process | 460_4_59 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | If set to `True`, the dataloader prepared by the Accelerator is only iterated through on the main process
and then the batches are split and broadcast to each process. Will default to `True` for `DataLoader` whose
underlying dataset is an `IterableDataset`, `False` otherwise.
- even_batches (`bool`, *optional*, defaults to `True`):
If set to `True`, in cases where the total batch size across all processes does not exactly divide the | 460_4_60 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | If set to `True`, in cases where the total batch size across all processes does not exactly divide the
dataset, samples at the start of the dataset will be duplicated so the batch can be divided equally among
all workers.
- use_seedable_sampler (`bool`, *optional*, defaults to `True`):
Whether or not use a fully seedable random sampler ([`accelerate.data_loader.SeedableRandomSampler`]). Ensures
training results are fully reproducable using a different sampling technique. While seed-to-seed results | 460_4_61 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | training results are fully reproducable using a different sampling technique. While seed-to-seed results
may differ, on average the differences are neglible when using multiple different seeds to compare. Should
also be ran with [`~utils.set_seed`] for the best results.
- use_configured_state (`bool`, *optional*, defaults to `False`):
Whether or not to use a pre-configured `AcceleratorState` or `PartialState` defined before calling `TrainingArguments`. | 460_4_62 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | Whether or not to use a pre-configured `AcceleratorState` or `PartialState` defined before calling `TrainingArguments`.
If `True`, an `Accelerator` or `PartialState` must be initialized. Note that by doing so, this could lead to issues
with hyperparameter tuning.
label_smoothing_factor (`float`, *optional*, defaults to 0.0):
The label smoothing factor to use. Zero means no label smoothing, otherwise the underlying onehot-encoded | 460_4_63 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | The label smoothing factor to use. Zero means no label smoothing, otherwise the underlying onehot-encoded
labels are changed from 0s and 1s to `label_smoothing_factor/num_labels` and `1 - label_smoothing_factor +
label_smoothing_factor/num_labels` respectively.
debug (`str` or list of [`~debug_utils.DebugOption`], *optional*, defaults to `""`):
Enable one or more debug features. This is an experimental feature.
Possible options are: | 460_4_64 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | Enable one or more debug features. This is an experimental feature.
Possible options are:
- `"underflow_overflow"`: detects overflow in model's input/outputs and reports the last frames that led to
the event
- `"tpu_metrics_debug"`: print debug metrics on TPU
The options should be separated by whitespaces.
optim (`str` or [`training_args.OptimizerNames`], *optional*, defaults to `"adamw_torch"`): | 460_4_65 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | .md | optim (`str` or [`training_args.OptimizerNames`], *optional*, defaults to `"adamw_torch"`):
The optimizer to use, such as "adamw_hf", "adamw_torch", "adamw_torch_fused", "adamw_apex_fused", "adamw_anyprecision",
"adafactor". See `OptimizerNames` in [training_args.py](https://github.com/huggingface/transformers/blob/main/src/transformers/training_args.py)
for a full list of optimizers.
optim_args (`str`, *optional*): | 460_4_66 |
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