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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 SquadV2P... | 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... | 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... | 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 ... | 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 dat... | 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_... | 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 handl... | 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/tran... | 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 agr... | 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 ... | 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.... | 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 h... | 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 [`TrainingArgum... | 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 wor... | 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* i... | 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.... | 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 int... | 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.Datase... | 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 `BaseImageProcesso... | 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 (`C... | 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 a... | 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://githu... | 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 ... | 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
d... | 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`]... | 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... | 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 f... | 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 mor... | 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... | 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 t... | 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:
... | 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... | 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 ... | 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... | 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"`: ... | 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 tr... | 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 ... | 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 tens... | 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, c... | 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 (`flo... | 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`] opt... | 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
th... | 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_... | 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 to... | 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',... | 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 t... | 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. ... | 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... | 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 lo... | 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. Po... | 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*, default... | 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 o... | 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. ... | 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*, ... | 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`):
... | 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*, defaul... | 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 r... | 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 ... | 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 ... | 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... | 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... | 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 ... | 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_g... | 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 (... | 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 sa... | 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](../mo... | 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 des... | 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 def... | 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.
... | 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`)... | 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... | 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 `... | 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 ... | 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, whethe... | 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 Distribute... | 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 no... | 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 Tra... | 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 ... | 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 parameter... | 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 ... | 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 interspe... | 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... | 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 b... | 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 r... | 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 ... | 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 ... | 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 ... | 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.... | 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... | 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 `... | 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*, default... | 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 seedab... | 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*, defau... | 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 ... | 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`], *opt... | 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 ... | 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/mai... | 460_4_66 |
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