PretrainedConfig

ot be used.   force_download (bool, optional, defaults to False) —
Whether or not to force to (re-)download the configuration files and override the cached versions if
they exist.   resume_download (bool, optional, defaults to False) —
Whether or not to delete incompletely received file. Attempts to resume the download if such a file
exists.   proxies (Dict[str, str], optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}. The proxies are used on each request.   token (str or bool, optional) —
The token to use as HTTP bearer authorization for remote files. If True, or not specified, will use
the token generated when running huggingface-cli login (stored in ~/.huggingface).   revision (str, optional, defaults to "main") —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on


PretrainedConfig

huggingface.co, so revision can be any
identifier allowed by git.
To test a pull request you made on the Hub, you can pass `revision=“refs/pr/“.
   return_unused_kwargs (bool, optional, defaults to False) —
If False, then this function returns just the final configuration object.
If True, then this functions returns a Tuple(config, unused_kwargs) where unused_kwargs is a
dictionary consisting of the key/value pairs whose keys are not configuration attributes: i.e., the
part of kwargs which has not been used to update config and is otherwise ignored.   subfolder (str, optional, defaults to "") —
In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can
specify the folder name here.   kwargs (Dict[str, Any], optional) —
The values in kwargs of any keys which are configuration attributes will be used to override the loaded
values. Behavior concerning key/value pairs whose keys are not configuration attributes is controlled


PretrainedConfig

by the return_unused_kwargs keyword parameter.   Returns
PretrainedConfig

The configuration object instantiated from this pretrained model.
  Instantiate a PretrainedConfig (or a derived class) from a pretrained model configuration.  Examples:   Copied # We can't instantiate directly the base class *PretrainedConfig* so let's show the examples on a
# derived class: BertConfig
config = BertConfig.from_pretrained(
    "bert-base-uncased"
)  # Download configuration from huggingface.co and cache.
config = BertConfig.from_pretrained(
    "./test/saved_model/"
)  # E.g. config (or model) was saved using *save_pretrained('./test/saved_model/')*
config = BertConfig.from_pretrained("./test/saved_model/my_configuration.json")
config = BertConfig.from_pretrained("bert-base-uncased", output_attentions=True, foo=False)
assert config.output_attentions == True
config, unused_kwargs = BertConfig.from_pretrained(
    "bert-base-uncased", output_attentions=True, foo=False, re


PretrainedConfig

turn_unused_kwargs=True
)
assert config.output_attentions == True
assert unused_kwargs == {"foo": False}  get_config_dict  < source > ( pretrained_model_name_or_path: typing.Union[str, os.PathLike] **kwargs  ) → Tuple[Dict, Dict]  Parameters   pretrained_model_name_or_path (str or os.PathLike) —
The identifier of the pre-trained checkpoint from which we want the dictionary of parameters.   Returns
Tuple[Dict, Dict]

The dictionary(ies) that will be used to instantiate the configuration object.
  From a pretrained_model_name_or_path, resolve to a dictionary of parameters, to be used for instantiating a
PretrainedConfig using from_dict.  register_for_auto_class  < source > ( auto_class = 'AutoConfig'  )   Parameters   auto_class (str or type, optional, defaults to "AutoConfig") —
The auto class to register this new configuration with.    Register this class with a given auto class. This should only be used for custom configurations as the ones in
the library are


PretrainedConfig

already mapped with AutoConfig. This API is experimental and may have some slight breaking changes in the next releases.  save_pretrained  < source > ( save_directory: typing.Union[str, os.PathLike] push_to_hub: bool = False **kwargs  )   Parameters   save_directory (str or os.PathLike) —
Directory where the configuration JSON file will be saved (will be created if it does not exist).   push_to_hub (bool, optional, defaults to False) —
Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the
repository you want to push to with repo_id (will default to the name of save_directory in your
namespace).   kwargs (Dict[str, Any], optional) —
Additional key word arguments passed along to the push_to_hub() method.    Save a configuration object to the directory save_directory, so that it can be re-loaded using the
from_pretrained() class method.  to_dict  < source > ( ) → Dict[str, Any]  Returns
Dict[str, Any]

Dictionary of a


PretrainedConfig

ll the attributes that make up this configuration instance.
  Serializes this instance to a Python dictionary.  to_diff_dict  < source > ( ) → Dict[str, Any]  Returns
Dict[str, Any]

Dictionary of all the attributes that make up this configuration instance,
  Removes all attributes from config which correspond to the default config attributes for better readability and
serializes to a Python dictionary.  to_json_file  < source > ( json_file_path: typing.Union[str, os.PathLike] use_diff: bool = True  )   Parameters   json_file_path (str or os.PathLike) —
Path to the JSON file in which this configuration instance’s parameters will be saved.   use_diff (bool, optional, defaults to True) —
If set to True, only the difference between the config instance and the default PretrainedConfig()
is serialized to JSON file.    Save this instance to a JSON file.  to_json_string  < source > ( use_diff: bool = True  ) → str  Parameters   use_diff (bool, optional, defaults to Tr


PretrainedConfig

ue) —
If set to True, only the difference between the config instance and the default PretrainedConfig()
is serialized to JSON string.   Returns
str

String containing all the attributes that make up this configuration instance in JSON format.
  Serializes this instance to a JSON string.  update  < source > ( config_dict: typing.Dict[str, typing.Any]  )   Parameters   config_dict (Dict[str, Any]) — Dictionary of attributes that should be updated for this class.    Updates attributes of this class with attributes from config_dict.  update_from_string  < source > ( update_str: str  )   Parameters   update_str (str) — String with attributes that should be updated for this class.    Updates attributes of this class with attributes from update_str. The expected format is ints, floats and strings as is, and for booleans use true or false. For example:
“n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index” The keys to change have to already exist


Image Processor

An image processor is in charge of preparing input features for vision models and post processing their outputs. This includes transformations such as resizing, normalization, and conversion to PyTorch, TensorFlow, Flax and Numpy tensors. It may also include model specific post-processing such as converting logits to segmentation masks.


ImageProcessingMixin

class transformers.ImageProcessingMixin  < source > ( **kwargs  )    This is an image processor mixin used to provide saving/loading functionality for sequential and image feature
extractors.  from_pretrained  < source > ( pretrained_model_name_or_path: typing.Union[str, os.PathLike] cache_dir: typing.Union[str, os.PathLike, NoneType] = None force_download: bool = False local_files_only: bool = False token: typing.Union[bool, str, NoneType] = None revision: str = 'main' **kwargs  )   Parameters   pretrained_model_name_or_path (str or os.PathLike) —
This can be either:
a string, the model id of a pretrained image_processor hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like bert-base-uncased, or
namespaced under a user or organization name, like dbmdz/bert-base-german


ImageProcessingMixin

-cased.
a path to a directory containing a image processor file saved using the
save_pretrained() method, e.g.,
./my_model_directory/.
a path or url to a saved image processor JSON file, e.g.,
./my_model_directory/preprocessor_config.json.
   cache_dir (str or os.PathLike, optional) —
Path to a directory in which a downloaded pretrained model image processor should be cached if the
standard cache should not be used.   force_download (bool, optional, defaults to False) —
Whether or not to force to (re-)download the image processor files and override the cached versions if
they exist.   resume_download (bool, optional, defaults to False) —
Whether or not to delete incompletely received file. Attempts to resume the download if such a file
exists.   proxies (Dict[str, str], optional) —
A dictionary of proxy servers to


ImageProcessingMixin

use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}. The proxies are used on each request.   token (str or bool, optional) —
The token to use as HTTP bearer authorization for remote files. If True, or not specified, will use
the token generated when running huggingface-cli login (stored in ~/.huggingface).   revision (str, optional, defaults to "main") —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision can be any
identifier allowed by git.    Instantiate a type of ImageProcessingMixin from an image processor.  Examples:   Copied # We can't instantiate directly the base class *ImageProcessingMixin* so let's show the examples on a
# der


ImageProcessingMixin

ived class: *CLIPImageProcessor*
image_processor = CLIPImageProcessor.from_pretrained(
    "openai/clip-vit-base-patch32"
)  # Download image_processing_config from huggingface.co and cache.
image_processor = CLIPImageProcessor.from_pretrained(
    "./test/saved_model/"
)  # E.g. image processor (or model) was saved using *save_pretrained('./test/saved_model/')*
image_processor = CLIPImageProcessor.from_pretrained("./test/saved_model/preprocessor_config.json")
image_processor = CLIPImageProcessor.from_pretrained(
    "openai/clip-vit-base-patch32", do_normalize=False, foo=False
)
assert image_processor.do_normalize is False
image_processor, unused_kwargs = CLIPImageProcessor.from_pretrained(
    "openai/clip-vit-base-patch32", do_normalize=False, foo=False, return_unused_kwargs=True
)
assert image_processor.do_norm


ImageProcessingMixin

alize is False
assert unused_kwargs == {"foo": False}  save_pretrained  < source > ( save_directory: typing.Union[str, os.PathLike] push_to_hub: bool = False **kwargs  )   Parameters   save_directory (str or os.PathLike) —
Directory where the image processor JSON file will be saved (will be created if it does not exist).   push_to_hub (bool, optional, defaults to False) —
Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the
repository you want to push to with repo_id (will default to the name of save_directory in your
namespace).   kwargs (Dict[str, Any], optional) —
Additional key word arguments passed along to the push_to_hub() method.    Save an image processor object to the directory save_directory, so that it can be re-loaded using the
from_pretrained() class met


BatchFeature

class transformers.BatchFeature  < source > ( data: typing.Union[typing.Dict[str, typing.Any], NoneType] = None tensor_type: typing.Union[NoneType, str, transformers.utils.generic.TensorType] = None  )   Parameters   data (dict, optional) —
Dictionary of lists/arrays/tensors returned by the call/pad methods (‘input_values’, ‘attention_mask’,
etc.).   tensor_type (Union[None, str, TensorType], optional) —
You can give a tensor_type here to convert the lists of integers in PyTorch/TensorFlow/Numpy Tensors at
initialization.    Holds the output of the pad() and feature extractor specific __call__ methods. This class is derived from a python dictionary and can be used as a dictionary.  convert_to_tensors  < source > ( tensor_type: typing.Uni


BatchFeature

on[str, transformers.utils.generic.TensorType, NoneType] = None  )   Parameters   tensor_type (str or TensorType, optional) —
The type of tensors to use. If str, should be one of the values of the enum TensorType. If
None, no modification is done.    Convert the inner content to tensors.  to  < source > ( *args **kwargs  ) → BatchFeature  Parameters   args (Tuple) —
Will be passed to the to(...) function of the tensors.   kwargs (Dict, optional) —
Will be passed to the to(...) function of the tensors.   Returns
BatchFeature

The same instance after modification.
  Send all values to device by calling v.to(*args, **kwargs) (PyTorch only). This should support casting in
different dtypes and sending the BatchFeature to a different device


BaseImageProcessor

class transformers.image_processing_utils.BaseImageProcessor  < source > ( **kwargs  )     center_crop  < source > ( image: ndarray size: typing.Dict[str, int] data_format: typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None input_data_format: typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None **kwargs  )   Parameters   image (np.ndarray) —
Image to center crop.   size (Dict[str, int]) —
Size of the output image.   data_format (str or ChannelDimension, optional) —
The channel dimension format for the output image. If unset, the channel dimension format of the input
image is used. Can be one of:
"channels_first" or ChannelDimension.FIRST: image in (num_channels, height, width) format.
"channels_last" or ChannelDimension.LAST: image in (height, w


BaseImageProcessor

idth, num_channels) format.
   input_data_format (ChannelDimension or str, optional) —
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
"channels_first" or ChannelDimension.FIRST: image in (num_channels, height, width) format.
"channels_last" or ChannelDimension.LAST: image in (height, width, num_channels) format.
    Center crop an image to (size["height"], size["width"]). If the input size is smaller than crop_size along
any edge, the image is padded with 0’s and then center cropped.  normalize  < source > ( image: ndarray mean: typing.Union[float, typing.Iterable[float]] std: typing.Union[float, typing.Iterable[float]] data_format: typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = No


BaseImageProcessor

ne input_data_format: typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None **kwargs  ) → np.ndarray  Parameters   image (np.ndarray) —
Image to normalize.   mean (float or Iterable[float]) —
Image mean to use for normalization.   std (float or Iterable[float]) —
Image standard deviation to use for normalization.   data_format (str or ChannelDimension, optional) —
The channel dimension format for the output image. If unset, the channel dimension format of the input
image is used. Can be one of:
"channels_first" or ChannelDimension.FIRST: image in (num_channels, height, width) format.
"channels_last" or ChannelDimension.LAST: image in (height, width, num_channels) format.
   input_data_format (ChannelDimension or str, optional) —
The channel dimension format for the input i


BaseImageProcessor

mage. If unset, the channel dimension format is inferred
from the input image. Can be one of:
"channels_first" or ChannelDimension.FIRST: image in (num_channels, height, width) format.
"channels_last" or ChannelDimension.LAST: image in (height, width, num_channels) format.
   Returns
np.ndarray

The normalized image.
  Normalize an image. image = (image - image_mean) / image_std.  rescale  < source > ( image: ndarray scale: float data_format: typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None input_data_format: typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None **kwargs  ) → np.ndarray  Parameters   image (np.ndarray) —
Image to rescale.   scale (float) —
The scaling factor to rescale pixel values by.   data_format (str or ChannelDimension,


BaseImageProcessor

 optional) —
The channel dimension format for the output image. If unset, the channel dimension format of the input
image is used. Can be one of:
"channels_first" or ChannelDimension.FIRST: image in (num_channels, height, width) format.
"channels_last" or ChannelDimension.LAST: image in (height, width, num_channels) format.
   input_data_format (ChannelDimension or str, optional) —
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
"channels_first" or ChannelDimension.FIRST: image in (num_channels, height, width) format.
"channels_last" or ChannelDimension.LAST: image in (height, width, num_channels) format.
   Returns
np.ndarray

The rescaled image.
  Rescale an image by a scale factor. image = image * scal


Trainer

The Trainer class provides an API for feature-complete training in PyTorch for most standard use cases. It’s used in most of the example scripts.
Before instantiating your Trainer, create a TrainingArguments to access all the points of customization during training.
The API supports distributed training on multiple GPUs/TPUs, mixed precision through NVIDIA Apex and Native AMP for PyTorch.
The Trainer contains the basic training loop which supports the above features. To inject custom behavior you can subclass them and override the following methods:
get_train_dataloader — Creates the training DataLoader. get_eval_dataloader — Creates the evaluation DataLoader. get_test_dataloader — Creates the test DataLoader. log — Logs information on the va


Trainer

rious objects watching training. create_optimizer_and_scheduler — Sets up the optimizer and learning rate scheduler if they were not passed at
init. Note, that you can also subclass or override the create_optimizer and create_scheduler methods
separately. create_optimizer — Sets up the optimizer if it wasn’t passed at init. create_scheduler — Sets up the learning rate scheduler if it wasn’t passed at init. compute_loss - Computes the loss on a batch of training inputs. training_step — Performs a training step. prediction_step — Performs an evaluation/test step. evaluate — Runs an evaluation loop and returns metrics. predict — Returns predictions (with metrics if labels are available) on a test set.
The Trainer class is optimized for 🤗 Transfo


Trainer

rmers models and can have surprising behaviors
when you use it on other models. When using it on your own model, make sure: your model always return tuples or subclasses of 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 the label_names in your TrainingArguments to indicate their name to the Trainer) but none of them should be named "label".
Here is an example of how to customize Trainer to use a weighted loss (useful when you have an unbalanced training set):
  Copied from torch import nn
from transformers import Trainer
class CustomTrainer(Trainer):
    def compute_loss(


Trainer

self, model, inputs, return_outputs=False):
        labels = inputs.pop("labels")
        # forward pass
        outputs = model(**inputs)
        logits = outputs.get("logits")
        # compute custom loss (suppose one has 3 labels with different weights)
        loss_fct = nn.CrossEntropyLoss(weight=torch.tensor([1.0, 2.0, 3.0], device=model.device))
        loss = loss_fct(logits.view(-1, self.model.config.num_labels), labels.view(-1))
        return (loss, outputs) if return_outputs else loss
Another way to customize the training loop behavior for the PyTorch Trainer is to use callbacks that can inspect the training loop state (for progress reporting, logging on TensorBoard or other ML platforms…) and take decisions (like early stopping)


Trainer

class transformers.Trainer  < source > ( model: typing.Union[transformers.modeling_utils.PreTrainedModel, torch.nn.modules.module.Module] = None args: TrainingArguments = None data_collator: typing.Optional[DataCollator] = None train_dataset: typing.Optional[torch.utils.data.dataset.Dataset] = None eval_dataset: typing.Union[torch.utils.data.dataset.Dataset, typing.Dict[str, torch.utils.data.dataset.Dataset], NoneType] = None tokenizer: typing.Optional[transformers.tokenization_utils_base.PreTrainedTokenizerBase] = None model_init: typing.Union[typing.Callable[[], transformers.modeling_utils.PreTrainedModel], NoneType] = None compute_metrics: typing.Union[typing.Callable[[transformers.trainer_utils.EvalPrediction], typing.Dict], NoneType] = None callbacks: typing.Optional[typing.List[transformers.trainer_callback.TrainerCallback]] = None optimizers: typing.Tuple[torch.optim.optimizer.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None) preprocess_logits_for_metrics: typing.U


Trainer

nion[typing.Callable[[torch.Tensor, torch.Tensor], torch.Tensor], NoneType] = None  )   Parameters   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.
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.
   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
default to default_data_collator() if no tokenizer is provided, an instance of
DataCollatorWithPadding otherwise.   train_dataset (torch.utils.data.Dataset or torch.u


Trainer

tils.data.IterableDataset, optional) —
The dataset to use for training. If it is a 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
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]), optional) —
The dataset to use for evaluation. If it is a 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.   tokenizer (PreTrainedTokenizerBase, optional) —
The


Trainer

 tokenizer used to preprocess the data. If provided, will be used to automatically pad the inputs to the
maximum length when batching inputs, and it will be saved along the model to make it easier to rerun an
interrupted training or reuse the fine-tuned model.   model_init (Callable[[], PreTrainedModel], optional) —
A function that instantiates the model to be used. If provided, each call to 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_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.   callbacks (List of TrainerCallback, optional) —
A list of call


Trainer

backs to customize the training loop. Will add those to the list of default callbacks
detailed in here.
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.   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.    Trai


Trainer

ner is a simple but feature-complete training and eval loop for PyTorch, optimized for 🤗 Transformers. 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,
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). 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
TrainingArgument


Trainer

s.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)  add_callback  < source > ( callback  )   Parameters   callback (type or ~transformer.TrainerCallback) —
A ~transformer.TrainerCallback class or an instance of a ~transformer.TrainerCallback. In the
first case, will instantiate a member of that class.    Add a callback to the current list of ~transformer.TrainerCallback.  autocast_smart_context_manager  < source > ( cache_enabled: typing.Optional[bool] = True  )    A helper wrapper that creates an appropriate context manager for autocast while feeding it the desired
arguments, depending on the situation.  compute_loss  < source > ( model inputs return_outputs = False  )    How the loss is computed by Trainer. By default, all models return the loss in the first element. Subclass and override for custom behavior.  compute_loss_context_manager  < source > ( )    A helper w


Trainer

rapper to group together context managers.  create_model_card  < source > ( language: typing.Optional[str] = None license: typing.Optional[str] = None tags: typing.Union[str, typing.List[str], NoneType] = None model_name: typing.Optional[str] = None finetuned_from: typing.Optional[str] = None tasks: typing.Union[str, typing.List[str], NoneType] = None dataset_tags: typing.Union[str, typing.List[str], NoneType] = None dataset: typing.Union[str, typing.List[str], NoneType] = None dataset_args: typing.Union[str, typing.List[str], NoneType] = None  )   Parameters   language (str, optional) —
The language of the model (if applicable)   license (str, optional) —
The license of the model. Will default to the license of the pretrained model used, if the original
model given to the Trainer comes from a repo on the Hub.   tags (str or List[str], optional) —
Some tags to be included in the metadata of the model card.   model_name (str, optional) —
The name of the model.   finetuned_from (str,


Trainer

optional) —
The name of the model used to fine-tune this one (if applicable). Will default to the name of the repo
of the original model given to the Trainer (if it comes from the Hub).   tasks (str or List[str], optional) —
One or several task identifiers, to be included in the metadata of the model card.   dataset_tags (str or List[str], optional) —
One or several dataset tags, to be included in the metadata of the model card.   dataset (str or List[str], optional) —
One or several dataset identifiers, to be included in the metadata of the model card.   dataset_args (str or List[str], optional) —
One or several dataset arguments, to be included in the metadata of the model card.    Creates a draft of a model card using the information available to the Trainer.  create_optimizer  < source > ( )    Setup the optimizer. We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
Trainer’s init through optimizers, or subclass and ove


Trainer

rride this method in a subclass.  create_optimizer_and_scheduler  < source > ( num_training_steps: int  )    Setup the optimizer and the learning rate scheduler. We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
Trainer’s init through optimizers, or subclass and override this method (or create_optimizer and/or
create_scheduler) in a subclass.  create_scheduler  < source > ( num_training_steps: int optimizer: Optimizer = None  )   Parameters   num_training_steps (int) — The number of training steps to do.    Setup the scheduler. The optimizer of the trainer must have been set up either before this method is called or
passed as an argument.  evaluate  < source > ( eval_dataset: typing.Optional[torch.utils.data.dataset.Dataset] = None ignore_keys: typing.Optional[typing.List[str]] = None metric_key_prefix: str = 'eval'  )   Parameters   eval_dataset (Dataset, optional) —
Pass a dataset if you wish to override self.eval_datas


Trainer

et. If it is a Dataset, columns
not accepted by the model.forward() method are automatically removed. It must implement the __len__
method.   ignore_keys (List[str], optional) —
A list of keys in the output of your model (if it is a dictionary) that should be ignored when
gathering predictions.   metric_key_prefix (str, optional, defaults to "eval") —
An optional prefix to be used as the metrics key prefix. For example the metrics “bleu” will be named
“eval_bleu” if the prefix is “eval” (default)    Run evaluation and returns metrics. The calling script will be responsible for providing a method to compute metrics, as they are task-dependent
(pass it to the init compute_metrics argument). You can also subclass and override this method to inject custom behavior.  evaluation_loop  < source > ( dataloader: DataLoader description: str prediction_loss_only: typing.Optional[bool] = None ignore_keys: typing.Optional[typing.List[str]] = None metric_key_prefix: str = 'eval'  )    Prediction/


Trainer

evaluation loop, shared by Trainer.evaluate() and Trainer.predict(). Works both with or without labels.  floating_point_ops  < source > ( inputs: typing.Dict[str, typing.Union[torch.Tensor, typing.Any]]  ) → int  Parameters   inputs (Dict[str, Union[torch.Tensor, Any]]) —
The inputs and targets of the model.   Returns
int

The number of floating-point operations.
  For models that inherit from PreTrainedModel, uses that method to compute the number of floating point
operations for every backward + forward pass. If using another model, either implement such a method in the
model or subclass and override this method.  get_decay_parameter_names  < source > ( model  )    Get all parameter names that weight decay will be applied to Note that some models implement their own layernorm instead of calling nn.LayerNorm, weight decay could still
apply to those modules since this function only filter out instance of nn.LayerNorm  get_eval_dataloader  < source > ( eval_dataset: typing.Optiona


Trainer

l[torch.utils.data.dataset.Dataset] = None  )   Parameters   eval_dataset (torch.utils.data.Dataset, optional) —
If provided, will override self.eval_dataset. If it is a Dataset, columns not accepted
by the model.forward() method are automatically removed. It must implement __len__.    Returns the evaluation ~torch.utils.data.DataLoader. Subclass and override this method if you want to inject some custom behavior.  get_optimizer_cls_and_kwargs  < source > ( args: TrainingArguments  )   Parameters   args (transformers.training_args.TrainingArguments) —
The training arguments for the training session.    Returns the optimizer class and optimizer parameters based on the training arguments.  get_test_dataloader  < source > ( test_dataset: Dataset  )   Parameters   test_dataset (torch.utils.data.Dataset, optional) —
The test dataset to use. If it is a Dataset, columns not accepted by the
model.forward() method are automatically removed. It must implement __len__.    Returns the test ~tor


Trainer

ch.utils.data.DataLoader. Subclass and override this method if you want to inject some custom behavior.  get_train_dataloader  < source > ( )    Returns the training ~torch.utils.data.DataLoader. Will use no sampler if train_dataset does not implement __len__, a random sampler (adapted to distributed
training if necessary) otherwise. Subclass and override this method if you want to inject some custom behavior.  hyperparameter_search  < source > ( hp_space: typing.Union[typing.Callable[[ForwardRef('optuna.Trial')], typing.Dict[str, float]], NoneType] = None compute_objective: typing.Union[typing.Callable[[typing.Dict[str, float]], float], NoneType] = None n_trials: int = 20 direction: typing.Union[str, typing.List[str]] = 'minimize' backend: typing.Union[ForwardRef('str'), transformers.trainer_utils.HPSearchBackend, NoneType] = None hp_name: typing.Union[typing.Callable[[ForwardRef('optuna.Trial')], str], NoneType] = None **kwargs  ) → [trainer_utils.BestRun or List[trainer_utils.Bes


Trainer

tRun]]  Parameters   hp_space (Callable[["optuna.Trial"], Dict[str, float]], optional) —
A function that defines the hyperparameter search space. Will default to
default_hp_space_optuna() or default_hp_space_ray() or
default_hp_space_sigopt() depending on your backend.   compute_objective (Callable[[Dict[str, float]], float], optional) —
A function computing the objective to minimize or maximize from the metrics returned by the evaluate
method. Will default to default_compute_objective().   n_trials (int, optional, defaults to 100) —
The number of trial runs to test.   direction (str or List[str], optional, defaults to "minimize") —
If it’s single objective optimization, direction is str, can be "minimize" or "maximize", you
should pick "minimize" when optimizing the validation loss, "maximize" when optimizing one or
several metrics. If it’s multi objectives optimization, direction is List[str], can be List of
"minimize" and "maximize", you should pick "minimize" when optimizing the


Trainer

 validation loss,
"maximize" when optimizing one or several metrics.   backend (str or ~training_utils.HPSearchBackend, optional) —
The backend to use for hyperparameter search. Will default to optuna or Ray Tune or SigOpt, depending
on which one is installed. If all are installed, will default to optuna.   hp_name (Callable[["optuna.Trial"], str]], optional) —
A function that defines the trial/run name. Will default to None.   kwargs (Dict[str, Any], optional) —
Additional keyword arguments passed along to optuna.create_study or ray.tune.run. For more
information see:
the documentation of
optuna.create_study
the documentation of tune.run
the documentation of sigopt
   Returns
[trainer_utils.BestRun or List[trainer_utils.BestRun]]

All the information about the best run or best
runs for multi-objective optimization. Experiment summary can be found in run_summary attribute for Ray
backend.
  Launch an hyperparameter search using optuna or Ray Tune or SigOpt. The optimized quantity


Trainer

 is determined
by compute_objective, which defaults to a function returning the evaluation loss when no metric is provided,
the sum of all metrics otherwise. To use this method, you need to have provided a model_init when initializing your Trainer: we need to
reinitialize the model at each new run. This is incompatible with the optimizers argument, so you need to
subclass Trainer and override the method create_optimizer_and_scheduler() for custom
optimizer/scheduler.  init_git_repo  < source > ( at_init: bool = False  )   Parameters   at_init (bool, optional, defaults to False) —
Whether this function is called before any training or not. If self.args.overwrite_output_dir is
True and at_init is True, the path to the repo (which is self.args.output_dir) might be wiped
out.    Initializes a git repo in self.args.hub_model_id. This function is deprecated and will be removed in v4.34.0 of Transformers.  init_hf_repo  < source > ( )    Initializes a git repo in self.args.hub_model_id.  i


Trainer

s_local_process_zero  < source > ( )    Whether or not this process is the local (e.g., on one machine if training in a distributed fashion on several
machines) main process.  is_world_process_zero  < source > ( )    Whether or not this process is the global main process (when training in a distributed fashion on several
machines, this is only going to be True for one process).  log  < source > ( logs: typing.Dict[str, float]  )   Parameters   logs (Dict[str, float]) —
The values to log.    Log logs on the various objects watching training. Subclass and override this method to inject custom behavior.  log_metrics  < source > ( split metrics  )   Parameters   split (str) —
Mode/split name: one of train, eval, test   metrics (Dict[str, float]) —
The metrics returned from train/evaluate/predictmetrics: metrics dict    Log metrics in a specially formatted way Under distributed environment this is done only for a process with rank 0. Notes on memory reports: In order to get memory usage


Trainer

report you need to install psutil. You can do that with pip install psutil.  Now when this method is run, you will see a report that will include: :   Copied init_mem_cpu_alloc_delta   =     1301MB
init_mem_cpu_peaked_delta  =      154MB
init_mem_gpu_alloc_delta   =      230MB
init_mem_gpu_peaked_delta  =        0MB
train_mem_cpu_alloc_delta  =     1345MB
train_mem_cpu_peaked_delta =        0MB
train_mem_gpu_alloc_delta  =      693MB
train_mem_gpu_peaked_delta =        7MB Understanding the reports: the first segment, e.g., train__, tells you which stage the metrics are for. Reports starting with init_
will be added to the first stage that gets run. So that if only evaluation is run, the memory usage for the
__init__ will be reported along with the eval_ metrics. the third segment, is either cpu or gpu, tells you whether it’s the general RAM or the gpu0 memory
metric. *_alloc_delta - is the difference in the used/allocated memory counter between the end and the start of the
stage -


Trainer

it can be negative if a function released more memory than it allocated. *_peaked_delta - is any extra memory that was consumed and then freed - relative to the current allocated
memory counter - it is never negative. When you look at the metrics of any stage you add up alloc_delta +
peaked_delta and you know how much memory was needed to complete that stage. The reporting happens only for process of rank 0 and gpu 0 (if there is a gpu). Typically this is enough since the
main process does the bulk of work, but it could be not quite so if model parallel is used and then other GPUs may
use a different amount of gpu memory. This is also not the same under DataParallel where gpu0 may require much more
memory than the rest since it stores the gradient and optimizer states for all participating GPUS. Perhaps in the
future these reports will evolve to measure those too. The CPU RAM metric measures RSS (Resident Set Size) includes both the memory which is unique to the process and the
memo


Trainer

ry shared with other processes. It is important to note that it does not include swapped out memory, so the
reports could be imprecise. The CPU peak memory is measured using a sampling thread. Due to python’s GIL it may miss some of the peak memory if
that thread didn’t get a chance to run when the highest memory was used. Therefore this report can be less than
reality. Using tracemalloc would have reported the exact peak memory, but it doesn’t report memory allocations
outside of python. So if some C++ CUDA extension allocated its own memory it won’t be reported. And therefore it
was dropped in favor of the memory sampling approach, which reads the current process memory usage. The GPU allocated and peak memory reporting is done with torch.cuda.memory_allocated() and
torch.cuda.max_memory_allocated(). This metric reports only “deltas” for pytorch-specific allocations, as
torch.cuda memory management system doesn’t track any memory allocated outside of pytorch. For example, the very


Trainer

first cuda call typically loads CUDA kernels, which may take from 0.5 to 2GB of GPU memory. Note that this tracker doesn’t account for memory allocations outside of Trainer’s __init__, train,
evaluate and predict calls. Because evaluation calls may happen during train, we can’t handle nested invocations because
torch.cuda.max_memory_allocated is a single counter, so if it gets reset by a nested eval call, train’s tracker
will report incorrect info. If this pytorch issue gets resolved
it will be possible to change this class to be re-entrant. Until then we will only track the outer level of
train, evaluate and predict methods. Which means that if eval is called during train, it’s the latter
that will account for its memory usage and that of the former. This also means that if any other tool that is used along the Trainer calls
torch.cuda.reset_peak_memory_stats, the gpu peak memory stats could be invalid. And the Trainer will disrupt
the normal behavior of any such tools that rely o


Trainer

n calling torch.cuda.reset_peak_memory_stats themselves. For best performance you may want to consider turning the memory profiling off for production runs.  metrics_format  < source > ( metrics: typing.Dict[str, float]  ) → metrics (Dict[str, float])  Parameters   metrics (Dict[str, float]) —
The metrics returned from train/evaluate/predict   Returns
metrics (Dict[str, float])

The reformatted metrics
  Reformat Trainer metrics values to a human-readable format  num_examples  < source > ( dataloader: DataLoader  )    Helper to get number of samples in a ~torch.utils.data.DataLoader by accessing its dataset. When
dataloader.dataset does not exist or has no length, estimates as best it can  num_tokens  < source > ( train_dl: DataLoader max_steps: typing.Optional[int] = None  )    Helper to get number of tokens in a ~torch.utils.data.DataLoader by enumerating dataloader.  pop_callback  < source > ( callback  ) → ~transformer.TrainerCallback  Parameters   callback (type or ~transfor


Trainer

mer.TrainerCallback) —
A ~transformer.TrainerCallback class or an instance of a ~transformer.TrainerCallback. In the
first case, will pop the first member of that class found in the list of callbacks.   Returns
~transformer.TrainerCallback

The callback removed, if found.
  Remove a callback from the current list of ~transformer.TrainerCallback and returns it. If the callback is not found, returns None (and no error is raised).  predict  < source > ( test_dataset: Dataset ignore_keys: typing.Optional[typing.List[str]] = None metric_key_prefix: str = 'test'  )   Parameters   test_dataset (Dataset) —
Dataset to run the predictions on. If it is an datasets.Dataset, columns not accepted by the
model.forward() method are automatically removed. Has to implement the method __len__   ignore_keys (List[str], optional) —
A list of keys in the output of your model (if it is a dictionary) that should be ignored when
gathering predictions.   metric_key_prefix (str, optional, defaults to "test


Trainer

") —
An optional prefix to be used as the metrics key prefix. For example the metrics “bleu” will be named
“test_bleu” if the prefix is “test” (default)    Run prediction and returns predictions and potential metrics. Depending on the dataset and your use case, your test dataset may contain labels. In that case, this method
will also return metrics, like in evaluate(). If your predictions or labels have different sequence length (for instance because you’re doing dynamic padding
in a token classification task) the predictions will be padded (on the right) to allow for concatenation into
one array. The padding index is -100. Returns: NamedTuple A namedtuple with the following keys: predictions (np.ndarray): The predictions on test_dataset. label_ids (np.ndarray, optional): The labels (if the dataset contained some). metrics (Dict[str, float], optional): The potential dictionary of metrics (if the dataset contained
labels).  prediction_loop  < source > ( dataloader: DataLoader descrip


Trainer

tion: str prediction_loss_only: typing.Optional[bool] = None ignore_keys: typing.Optional[typing.List[str]] = None metric_key_prefix: str = 'eval'  )    Prediction/evaluation loop, shared by Trainer.evaluate() and Trainer.predict(). Works both with or without labels.  prediction_step  < source > ( model: Module inputs: typing.Dict[str, typing.Union[torch.Tensor, typing.Any]] prediction_loss_only: bool ignore_keys: typing.Optional[typing.List[str]] = None  ) → Tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]  Parameters   model (nn.Module) —
The model to evaluate.   inputs (Dict[str, Union[torch.Tensor, Any]]) —
The inputs and targets of the model.
The dictionary will be unpacked before being fed to the model. Most models expect the targets under the
argument labels. Check your model’s documentation for all accepted arguments.   prediction_loss_only (bool) —
Whether or not to return the loss only.   ignore_keys (List[str], optional) —
A list of keys in th


Trainer

e output of your model (if it is a dictionary) that should be ignored when
gathering predictions.   Returns
Tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]

A tuple with the loss,
logits and labels (each being optional).
  Perform an evaluation step on model using inputs. Subclass and override to inject custom behavior.  push_to_hub  < source > ( commit_message: typing.Optional[str] = 'End of training' blocking: bool = True **kwargs  )   Parameters   commit_message (str, optional, defaults to "End of training") —
Message to commit while pushing.   blocking (bool, optional, defaults to True) —
Whether the function should return only when the git push has finished.   kwargs (Dict[str, Any], optional) —
Additional keyword arguments passed along to create_model_card().    Upload self.model and self.tokenizer to the 🤗 model hub on the repo self.args.hub_model_id.  remove_callback  < source > ( callback  )   Parameters   callback (type or ~transformer.Trai


Trainer

nerCallback) —
A ~transformer.TrainerCallback class or an instance of a ~transformer.TrainerCallback. In the
first case, will remove the first member of that class found in the list of callbacks.    Remove a callback from the current list of ~transformer.TrainerCallback.  save_metrics  < source > ( split metrics combined = True  )   Parameters   split (str) —
Mode/split name: one of train, eval, test, all   metrics (Dict[str, float]) —
The metrics returned from train/evaluate/predict   combined (bool, optional, defaults to True) —
Creates combined metrics by updating all_results.json with metrics of this call    Save metrics into a json file for that split, e.g. train_results.json. Under distributed environment this is done only for a process with rank 0. To understand the metrics please read the docstring of log_metrics(). The only difference is that raw
unformatted numbers are saved in the current method.  save_model  < source > ( output_dir: typing.Optional[str] = None _internal_


Trainer

call: bool = False  )    Will save the model, so you can reload it using from_pretrained(). Will only save from the main process.  save_state  < source > ( )    Saves the Trainer state, since Trainer.save_model saves only the tokenizer with the model Under distributed environment this is done only for a process with rank 0.  train  < source > ( resume_from_checkpoint: typing.Union[bool, str, NoneType] = None trial: typing.Union[ForwardRef('optuna.Trial'), typing.Dict[str, typing.Any]] = None ignore_keys_for_eval: typing.Optional[typing.List[str]] = None **kwargs  )   Parameters   resume_from_checkpoint (str or bool, optional) —
If a str, local path to a saved checkpoint as saved by a previous instance of Trainer. If a
bool and equals True, load the last checkpoint in args.output_dir as saved by a previous instance
of Trainer. If present, training will resume from the model/optimizer/scheduler states loaded here.   trial (optuna.Trial or Dict[str, Any], optional) —
The trial run or t


Trainer

he hyperparameter dictionary for hyperparameter search.   ignore_keys_for_eval (List[str], optional) —
A list of keys in the output of your model (if it is a dictionary) that should be ignored when
gathering predictions for evaluation during the training.   kwargs (Dict[str, Any], optional) —
Additional keyword arguments used to hide deprecated arguments    Main training entry point.  training_step  < source > ( model: Module inputs: typing.Dict[str, typing.Union[torch.Tensor, typing.Any]]  ) → torch.Tensor  Parameters   model (nn.Module) —
The model to train.   inputs (Dict[str, Union[torch.Tensor, Any]]) —
The inputs and targets of the model.
The dictionary will be unpacked before being fed to the model. Most models expect the targets under the
argument labels. Check your model’s documentation for all accepted arguments.   Returns
torch.Tensor

The tensor with training loss on this batch.
  Perform a training step on a batch of inputs. Subclass and override to inject custom beh


Seq2SeqTrainer

class transformers.Seq2SeqTrainer  < source > ( model: typing.Union[ForwardRef('PreTrainedModel'), torch.nn.modules.module.Module] = None args: TrainingArguments = None data_collator: typing.Optional[ForwardRef('DataCollator')] = None train_dataset: typing.Optional[torch.utils.data.dataset.Dataset] = None eval_dataset: typing.Union[torch.utils.data.dataset.Dataset, typing.Dict[str, torch.utils.data.dataset.Dataset], NoneType] = None tokenizer: typing.Optional[ForwardRef('PreTrainedTokenizerBase')] = None model_init: typing.Union[typing.Callable[[], ForwardRef('PreTrainedModel')], NoneType] = None compute_metrics: typing.Union[typing.Callable[[ForwardRef('EvalPrediction')], typing.Dict], NoneType] = None callbacks: typing.Optional[typing.List[ForwardRef('TrainerCallback')]] = None optimizers: typing.Tuple[torch.optim.optimizer.Opt


Seq2SeqTrainer

imizer, torch.optim.lr_scheduler.LambdaLR] = (None, None) preprocess_logits_for_metrics: typing.Union[typing.Callable[[torch.Tensor, torch.Tensor], torch.Tensor], NoneType] = None  )     evaluate  < source > ( eval_dataset: typing.Optional[torch.utils.data.dataset.Dataset] = None ignore_keys: typing.Optional[typing.List[str]] = None metric_key_prefix: str = 'eval' **gen_kwargs  )   Parameters   eval_dataset (Dataset, optional) —
Pass a dataset if you wish to override self.eval_dataset. If it is an Dataset, columns
not accepted by the model.forward() method are automatically removed. It must implement the __len__
method.   ignore_keys (List[str], optional) —
A list of keys in the output of your model (if it is a dictionary) that should be ignored when
gathering predictions.   metric_key_prefix (str, optional, defaults to "eval") —


Seq2SeqTrainer

An optional prefix to be used as the metrics key prefix. For example the metrics “bleu” will be named
“eval_bleu” if the prefix is "eval" (default)   max_length (int, optional) —
The maximum target length to use when predicting with the generate method.   num_beams (int, optional) —
Number of beams for beam search that will be used when predicting with the generate method. 1 means no
beam search.
gen_kwargs —
Additional generate specific kwargs.    Run evaluation and returns metrics. The calling script will be responsible for providing a method to compute metrics, as they are task-dependent
(pass it to the init compute_metrics argument). You can also subclass and override this method to inject custom behavior.  predict  < source > ( test_dataset: Dataset ignore_keys: typing.Optional[typing.List[str]] = None metric_key_prefix: st


Seq2SeqTrainer

r = 'test' **gen_kwargs  )   Parameters   test_dataset (Dataset) —
Dataset to run the predictions on. If it is a Dataset, columns not accepted by the
model.forward() method are automatically removed. Has to implement the method __len__   ignore_keys (List[str], optional) —
A list of keys in the output of your model (if it is a dictionary) that should be ignored when
gathering predictions.   metric_key_prefix (str, optional, defaults to "eval") —
An optional prefix to be used as the metrics key prefix. For example the metrics “bleu” will be named
“eval_bleu” if the prefix is "eval" (default)   max_length (int, optional) —
The maximum target length to use when predicting with the generate method.   num_beams (int, optional) —
Number of beams for beam search that will be used when predicting with the generate method. 1 means no
beam


Seq2SeqTrainer

 search.
gen_kwargs —
Additional generate specific kwargs.    Run prediction and returns predictions and potential metrics. Depending on the dataset and your use case, your test dataset may contain labels. In that case, this method
will also return metrics, like in evaluate(). If your predictions or labels have different sequence lengths (for instance because you’re doing dynamic
padding in a token classification task) the predictions will be padded (on the right) to allow for
concatenation into one array. The padding index is -100. Returns: NamedTuple A namedtuple with the following keys: predictions (np.ndarray): The predictions on test_dataset. label_ids (np.ndarray, optional): The labels (if the dataset contained some). metrics (Dict[str, float], optional): The potential dictionary of metrics (if the dataset contained
labels)


TrainingArguments

class transformers.TrainingArguments  < source > ( output_dir: str overwrite_output_dir: bool = False do_train: bool = False do_eval: bool = False do_predict: bool = False evaluation_strategy: typing.Union[transformers.trainer_utils.IntervalStrategy, str] = 'no' prediction_loss_only: bool = False per_device_train_batch_size: int = 8 per_device_eval_batch_size: int = 8 per_gpu_train_batch_size: typing.Optional[int] = None per_gpu_eval_batch_size: typing.Optional[int] = None gradient_accumulation_steps: int = 1 eval_accumulation_steps: typing.Optional[int] = None eval_delay: typing.Optional[float] = 0 learning_rate: float = 5e-05 weight_decay: float = 0.0 adam_beta1: float = 0.9 adam_beta2: float = 0.999 adam_epsilon: float = 1e-08 max_grad_norm: float = 1.0 num_train_epochs: float = 3.0 max_steps: int = -1 lr_scheduler_type: typing.Union[transformers.trainer_utils.SchedulerType, str] = 'linear' warmup_ratio: float = 0.0 warmup_steps: int = 0 log_level: typing.Optional[str


TrainingArguments

] = 'passive' log_level_replica: typing.Optional[str] = 'warning' log_on_each_node: bool = True logging_dir: typing.Optional[str] = None logging_strategy: typing.Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps' logging_first_step: bool = False logging_steps: float = 500 logging_nan_inf_filter: bool = True save_strategy: typing.Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps' save_steps: float = 500 save_total_limit: typing.Optional[int] = None save_safetensors: typing.Optional[bool] = False save_on_each_node: bool = False no_cuda: bool = False use_cpu: bool = False use_mps_device: bool = False seed: int = 42 data_seed: typing.Optional[int] = None jit_mode_eval: bool = False use_ipex: bool = False bf16: bool = False fp16: bool = False fp16_opt_level: str = 'O1' half_precision_backend: str = 'auto' bf16_full_eval: bool = False fp16_full_eval: bool = False tf32: typing.Optional[bool] = None local_rank: int = -1 ddp_backend: typing.Optional[


TrainingArguments

str] = None tpu_num_cores: typing.Optional[int] = None tpu_metrics_debug: bool = False debug: typing.Union[str, typing.List[transformers.debug_utils.DebugOption]] = '' dataloader_drop_last: bool = False eval_steps: typing.Optional[float] = None dataloader_num_workers: int = 0 past_index: int = -1 run_name: typing.Optional[str] = None disable_tqdm: typing.Optional[bool] = None remove_unused_columns: typing.Optional[bool] = True label_names: typing.Optional[typing.List[str]] = None load_best_model_at_end: typing.Optional[bool] = False metric_for_best_model: typing.Optional[str] = None greater_is_better: typing.Optional[bool] = None ignore_data_skip: bool = False fsdp: typing.Union[typing.List[transformers.trainer_utils.FSDPOption], str, NoneType] = '' fsdp_min_num_params: int = 0 fsdp_config: typing.Optional[str] = None fsdp_transformer_layer_cls_to_wrap: typing.Optional[str] = None deepspeed: typing.Optional[str] = None label_smoothing_factor: float = 0.0 optim: typing.Un


TrainingArguments

ion[transformers.training_args.OptimizerNames, str] = 'adamw_torch' optim_args: typing.Optional[str] = None adafactor: bool = False group_by_length: bool = False length_column_name: typing.Optional[str] = 'length' report_to: typing.Optional[typing.List[str]] = None ddp_find_unused_parameters: typing.Optional[bool] = None ddp_bucket_cap_mb: typing.Optional[int] = None ddp_broadcast_buffers: typing.Optional[bool] = None dataloader_pin_memory: bool = True skip_memory_metrics: bool = True use_legacy_prediction_loop: bool = False push_to_hub: bool = False resume_from_checkpoint: typing.Optional[str] = None hub_model_id: typing.Optional[str] = None hub_strategy: typing.Union[transformers.trainer_utils.HubStrategy, str] = 'every_save' hub_token: typing.Optional[str] = None hub_private_repo: bool = False hub_always_push: bool = False gradient_checkpointing: bool = False include_inputs_for_metrics: bool = False fp16_backend: str = 'auto' push_to_hub_model_id: typing.Optional[str]


TrainingArguments

 = None push_to_hub_organization: typing.Optional[str] = None push_to_hub_token: typing.Optional[str] = None mp_parameters: str = '' auto_find_batch_size: bool = False full_determinism: bool = False torchdynamo: typing.Optional[str] = None ray_scope: typing.Optional[str] = 'last' ddp_timeout: typing.Optional[int] = 1800 torch_compile: bool = False torch_compile_backend: typing.Optional[str] = None torch_compile_mode: typing.Optional[str] = None dispatch_batches: typing.Optional[bool] = None include_tokens_per_second: typing.Optional[bool] = False  )   Parameters   output_dir (str) —
The output directory where the model predictions and checkpoints will be written.   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


TrainingArguments

 Trainer, it’s intended to be used
by your training/evaluation scripts instead. See the example
scripts for more details.   do_eval (bool, optional) —
Whether to run evaluation on the validation set or not. Will be set to True if evaluation_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
scripts 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 for more details.   evaluation_strategy (str or 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


TrainingArguments

 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.   gradient_accumulation_steps (int, optional, defaults to 1) —
Number of updates steps to accumulate the gradients for, before performing a backward/update pass.
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.
   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/NP


TrainingArguments

U/TPU before being moved to the CPU (faster but
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
evaluation_strategy.   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.   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) —
Maximum gradient norm (for gradient clipping).   num_train_epochs(float, optional, defaults to 3.0) —
Total number of training epochs to


TrainingArguments

 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) —
If set to a positive number, the total number of training steps to perform. Overrides num_train_epochs.
In case of using a finite iterable dataset the training may stop before reaching the set number of steps
when all data is exhausted   lr_scheduler_type (str or SchedulerType, optional, defaults to "linear") —
The scheduler type to use. See the documentation of SchedulerType for all 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) —
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 str


TrainingArguments

ings: ‘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).   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 log directory. Will default to
*output_dir/runs/CURRENT_DATETIME_HOSTNAME*.   logging_strategy (str or 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.
   logging_first_step (bool, optional, defaults to False) —
Whether to log and evalua


TrainingArguments

te 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.   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.
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.
   save_strategy (str or IntervalStrategy, 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.
   save_steps (int or fl


TrainingArguments

oat, optional, defaults to 500) —
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
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).   save_safetensors (bool, optional, defaults to False) —
Use safetensors saving and loading for state dicts instead of
default torch.load and torch.save


TrainingArguments

.   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.
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.   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) —
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, de


TrainingArguments

faults 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
installation.   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) —
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.   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 tra


TrainingArguments

ining. 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) —
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) —
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 documentation. This is an
experimental API and it may change.   local_rank (int, optional, defaults to -1) —
Rank of the process during distributed t


TrainingArguments

raining.   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) —
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 evaluation_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.   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 Tra


TrainingArguments

nsformerXL or XLNet can make use of
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) —
A descriptor for the run. Typically used for wandb and
mlflow logging.   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) —
Whether or not to automatically remove the columns unused by the model forward method.
(Note that this behavior is not implemented for TFTrainer yet.)   label_names (List[str], optional) —
The list of keys in your dictionary of inputs that correspond to the labels.
W


TrainingArguments

ill 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
save_total_limit
for more.
When set to True, the parameters save_strategy needs to be the same as evaluation_strategy, and in
the case it is “steps”, save_steps must be a round multiple of eval_steps.
   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_". Will
default to "loss" if unspecified and load_best_model_at_end=Tr


TrainingArguments

ue (to use the evaluation loss).
If you set this value, greater_is_better will default to True. 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:
True if metric_for_best_model is set to a value that isn’t "loss" or "eval_loss".
False if metric_for_best_model is not set, or set to "loss" or "eval_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
can take a long time) but will not yield the same results as the interrupted training would have.   fsdp (bool, str or list of FSDPOption, optional, defaults to '') —
Use PyTorch Distributed Parallel


TrainingArguments

 Training (in distributed training only).
A list of options along the following:
"full_shard": Shard parameters, gradients and optimizer states.
"shard_grad_op": Shard optimizer states and gradients.
"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
deepspeed json config file (e.g., ds_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 on


TrainingArguments

ly 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.
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).
If "True", FSDP explicitly synchronizes the CPU thread to prevent too many in-flight
all-gathers.
use_orig_params (bool, optional, defaults to False)
If "True", allows non-un


TrainingArguments

iform 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
[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
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.
xla_fsdp_grad_ckpt (bool, optional, defaults to False):
Will use gradient checkpointing over each nested XLA FSDP wrapped layer. This setting


TrainingArguments

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.
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.
   deepspeed (str or dict, optional) —
Use 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”   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
labels are changed from 0s and 1s to label_smoothing_factor/num_labels and 1 - label_smoothing_factor + label_s


TrainingArguments

moothing_factor/num_labels respectively.   debug (str or list of DebugOption, optional, defaults to "") —
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") —
The optimizer to use: adamw_hf, adamw_torch, adamw_torch_fused, adamw_apex_fused, adamw_anyprecision or
adafactor.   optim_args (str, optional) —
Optional arguments that are supplied to AnyPrecisionAdamW.   group_by_length (bool, optional, defaults to False) —
Whether or not to group together samples of roughly the same length in the training dataset (to minimize
padding applied and be more efficient). Only useful if applying dynamic padding.   length_column_name (str, optional, defaults t


TrainingArguments

o "length") —
Column name for precomputed lengths. If the column exists, grouping by length will use these values rather
than computing them on train startup. Ignored unless group_by_length is True and the dataset is an
instance of Dataset.   report_to (str or List[str], optional, defaults to "all") —
The list of integrations to report the results and logs to. Supported platforms are "azure_ml",
"clearml", "codecarbon", "comet_ml", "dagshub", "flyte", "mlflow", "neptune",
"tensorboard", and "wandb". Use "all" to report to all integrations installed, "none" for no
integrations.   ddp_find_unused_parameters (bool, optional) —
When using distributed training, the value of the flag find_unused_parameters passed to
DistributedDataParallel. Will default to False if gradient checkpointing is used, True otherwise.   ddp_bucket_cap_mb (int, optional) —
When using distributed training, the value of the flag bucket_cap_mb passed to DistributedDataParallel.   ddp_broadcast_buffers (


TrainingArguments

bool, optional) —
When using distributed training, the value of the flag broadcast_buffers passed to
DistributedDataParallel. Will default to False if gradient checkpointing is used, True otherwise.   dataloader_pin_memory (bool, optional, defaults to True) —
Whether you want to pin memory in data loaders or not. Will default to True.   skip_memory_metrics (bool, optional, defaults to True) —
Whether to skip adding of memory profiler reports to metrics. This is skipped by default because it slows
down the training and evaluation speed.   push_to_hub (bool, optional, defaults to False) —
Whether or not to push the model to the Hub every time the model is saved. If this is activated,
output_dir will begin a git directory synced with the repo (determined by hub_model_id) and the content
will be pushed each time a save is triggered (depending on your save_strategy). Calling
save_model() will also trigger a push.
If output_dir exists, it needs to be a local clone of the repos


TrainingArguments

itory to which the Trainer will be
pushed.
   resume_from_checkpoint (str, optional) —
The path to a folder with a valid checkpoint for your model. This argument is not directly used by
Trainer, it’s intended to be used by your training/evaluation scripts instead. See the example
scripts for more details.   hub_model_id (str, optional) —
The name of the repository to keep in sync with the local output_dir. It can be a simple model ID in
which case the model will be pushed in your namespace. Otherwise it should be the whole repository name,
for instance "user_name/model", which allows you to push to an organization you are a member of with
"organization_name/model". Will default to user_name/output_dir_name with output_dir_name being the
name of output_dir.
Will default to the name of output_dir.   hub_strategy (str or HubStrategy, optional, defaults to "every_save") —
Defines the scope of what is pushed to the Hub and when. Possible values are:
"end": push the model, its


TrainingArguments

 configuration, the tokenizer (if passed along to the Trainer) and a
draft of a model card when the save_model() method is called.
"every_save": push the model, its configuration, the tokenizer (if passed along to the Trainer) and
a draft of a model card each time there is a model save. The pushes are asynchronous to not block
training, and in case the save are very frequent, a new push is only attempted if the previous one is
finished. A last push is made with the final model at the end of training.
"checkpoint": like "every_save" but the latest checkpoint is also pushed in a subfolder named
last-checkpoint, allowing you to resume training easily with
trainer.train(resume_from_checkpoint="last-checkpoint").
"all_checkpoints": like "checkpoint" but all checkpoints are pushed like they appear in the output
folder (so you will get one checkpoint folder per folder in your final repository)
   hub_token (str, optional) —
The token to use to push the model to the Hub. Will de


TrainingArguments

fault to the token in the cache folder obtained with
huggingface-cli login.   hub_private_repo (bool, optional, defaults to False) —
If True, the Hub repo will be set to private.   hub_always_push (bool, optional, defaults to False) —
Unless this is True, the Trainer will skip pushing a checkpoint when the previous push is not finished.   gradient_checkpointing (bool, optional, defaults to False) —
If True, use gradient checkpointing to save memory at the expense of slower backward pass.   include_inputs_for_metrics (bool, optional, defaults to False) —
Whether or not the inputs will be passed to the compute_metrics function. This is intended for metrics
that need inputs, predictions and references for scoring calculation in Metric class.   auto_find_batch_size (bool, optional, defaults to False) —
Whether to find a batch size that will fit into memory automatically through exponential decay, avoiding
CUDA Out-of-Memory errors. Requires accelerate to be installed (pip in


TrainingArguments

stall accelerate)   full_determinism (bool, optional, defaults to False) —
If True, enable_full_determinism() is called instead of set_seed() to ensure reproducible results in
distributed training. Important: this will negatively impact the performance, so only use it for debugging.   torchdynamo (str, optional) —
If set, the backend compiler for TorchDynamo. Possible choices are "eager", "aot_eager", "inductor",
"nvfuser", "aot_nvfuser", "aot_cudagraphs", "ofi", "fx2trt", "onnxrt" and "ipex".   ray_scope (str, optional, defaults to "last") —
The scope to use when doing hyperparameter search with Ray. By default, "last" will be used. Ray will
then use the last checkpoint of all trials, compare those, and select the best one. However, other options
are also available. See the Ray documentation for
more options.   ddp_timeout (int, optional, defaults to 1800) —
The timeout for torch.distributed.init_process_group calls, used to avoid GPU socket timeouts when
performing slo


TrainingArguments

w operations in distributed runnings. Please refer the [PyTorch documentation]
(https://pytorch.org/docs/stable/distributed.html#torch.distributed.init_process_group) for more
information.   use_mps_device (bool, optional, defaults to False) —
This argument is deprecated.mps device will be used if it is available similar to cuda device.   torch_compile (bool, optional, defaults to False) —
Whether or not to compile the model using PyTorch 2.0
torch.compile.
This will use the best defaults for the torch.compile
API.
You can customize the defaults with the argument torch_compile_backend and torch_compile_mode but we
don’t guarantee any of them will work as the support is progressively rolled in in PyTorch.
This flag and the whole compile API is experimental and subject to change in future releases.   torch_compile_backend (str, optional) —
The backend to use in torch.compile. If set to any value, torch_compile will be set to True.
Refer to the PyTorch doc for possible valu


TrainingArguments

es and note that they may change across PyTorch versions.
This flag is experimental and subject to change in future releases.   torch_compile_mode (str, optional) —
The mode to use in torch.compile. If set to any value, torch_compile will be set to True.
Refer to the PyTorch doc for possible values and note that they may change across PyTorch versions.
This flag is experimental and subject to change in future releases.   include_tokens_per_second (bool, optional) —
Whether or not to compute the number of tokens per second per device for training speed metrics.
This will iterate over the entire training dataloader once beforehand,
and will slow down the entire process.    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 arguments that can be specified on the
command line.  get_process_log_level  < source > ( )    Returns the log level to be u


TrainingArguments

sed depending on whether this process is the main process of node 0, main process
of node non-0, or a non-main process. For the main process the log level defaults to the logging level set (logging.WARNING if you didn’t do
anything) unless overridden by log_level argument. For the replica processes the log level defaults to logging.WARNING unless overridden by log_level_replica
argument. The choice between the main and replica process settings is made according to the return value of should_log.  get_warmup_steps  < source > ( num_training_steps: int  )    Get number of steps used for a linear warmup.  main_process_first  < source > ( local = True desc = 'work'  )   Parameters   local (bool, optional, defaults to True) —
if True first means process of rank 0 of each node if False first means process of rank 0 of node
rank 0 In multi-node environment with a shared filesystem you most likely will want to use
local=False so that only the main process of the first node will


TrainingArguments

do the processing. If however, the
filesystem is not shared, then the main process of each node will need to do the processing, which is
the default behavior.   desc (str, optional, defaults to "work") —
a work description to be used in debug logs    A context manager for torch distributed environment where on needs to do something on the main process, while
blocking replicas, and when it’s finished releasing the replicas. One such use is for datasets’s map feature which to be efficient should be run once on the main process,
which upon completion saves a cached version of results and which then automatically gets loaded by the
replicas.  set_dataloader  < source > ( train_batch_size: int = 8 eval_batch_size: int = 8 drop_last: bool = False num_workers: int = 0 pin_memory: bool = True auto_find_batch_size: bool = False ignore_data_skip: bool = False sampler_seed: typing.Optional[int] = None  )   Parameters   drop_last (bool, optional, defaults to False) —
Whether to drop


TrainingArguments

 the last incomplete batch (if the length of the dataset is not divisible by the batch
size) or not.   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.   pin_memory (bool, optional, defaults to True) —
Whether you want to pin memory in data loaders or not. Will default to True.   auto_find_batch_size (bool, optional, defaults to False) —
Whether to find a batch size that will fit into memory automatically through exponential decay,
avoiding CUDA Out-of-Memory errors. Requires accelerate to be installed (pip install accelerate)   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 can take a long time) but will not yield the same results as the in


TrainingArguments

terrupted training
would have.   sampler_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 self.seed. This can be used to ensure reproducibility of data sampling, independent of
the model seed.    A method that regroups all arguments linked to the dataloaders creation.  Example:   Copied >>> from transformers import TrainingArguments
>>> args = TrainingArguments("working_dir")
>>> args = args.set_dataloader(train_batch_size=16, eval_batch_size=64)
>>> args.per_device_train_batch_size
16  set_evaluate  < source > ( strategy: typing.Union[str, transformers.trainer_utils.IntervalStrategy] = 'no' steps: int = 500 batch_size: int = 8 accumulation_steps: typing.Optional[int] = None delay: typing.Optional[float] = None loss_only: bool = False jit_mode: bool = False  )   Parameters   strategy (str or IntervalStrategy, optional, defaults to "no") —
The evaluation strategy to adopt during tra


TrainingArguments

ining. Possible values are:
"no": No evaluation is done during training.
"steps": Evaluation is done (and logged) every steps.
"epoch": Evaluation is done at the end of each epoch.
Setting a strategy different from "no" will set self.do_eval to True.   steps (int, optional, defaults to 500) —
Number of update steps between two evaluations if strategy="steps".   batch_size (int optional, defaults to 8) —
The batch size per device (GPU/TPU core/CPU…) used for evaluation.   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/TPU before being moved to the CPU (faster
but requires more memory).   delay (float, optional) —
Number of epochs or steps to wait for before the first evaluation can be performed, depending on the
evaluation_strategy.   loss_only (bool, optional, defaults to False) —
Ignores all outputs except the loss.


TrainingArguments

jit_mode (bool, optional) —
Whether or not to use PyTorch jit trace for inference.    A method that regroups all arguments linked to the evaluation.  Example:   Copied >>> from transformers import TrainingArguments
>>> args = TrainingArguments("working_dir")
>>> args = args.set_evaluate(strategy="steps", steps=100)
>>> args.eval_steps
100  set_logging  < source > ( strategy: typing.Union[str, transformers.trainer_utils.IntervalStrategy] = 'steps' steps: int = 500 report_to: typing.Union[str, typing.List[str]] = 'none' level: str = 'passive' first_step: bool = False nan_inf_filter: bool = False on_each_node: bool = False replica_level: str = 'passive'  )   Parameters   strategy (str or IntervalStrategy, optional, defaults to "steps") —
The logging 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.
   steps (int, optional, defaults to 500) —
N


TrainingArguments

umber of update steps between two logs if strategy="steps".   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 lets the application set the level.   report_to (str or List[str], optional, defaults to "none") —
The list of integrations to report the results and logs to. Supported platforms are "azure_ml",
"comet_ml", "mlflow", "neptune", "tensorboard","clearml" and "wandb". Use "all" to report
to all integrations installed, "none" for no integrations.   first_step (bool, optional, defaults to False) —
Whether to log and evaluate the first global_step or not.   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


TrainingArguments

is taken instead.
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.
   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.   replica_level (str, optional, defaults to "passive") —
Logger log level to use on replicas. Same choices as log_level    A method that regroups all arguments linked to the evaluation.  Example:   Copied >>> from transformers import TrainingArguments
>>> args = TrainingArguments("working_dir")
>>> args = args.set_logging(strategy="steps", steps=100)
>>> args.logging_steps
100  set_lr_scheduler  < source > ( name: typing.Union[str, transformers.trainer_utils.SchedulerType] = 'linear' num_epochs: float = 3.0 max_steps: int = -1 warmup_ratio: float = 0 warmup_steps: int = 0  )   Parameters   name (str or SchedulerType, optional, defaults to "linear") —
The sch


TrainingArguments

eduler type to use. See the documentation of SchedulerType for all possible values.   num_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) —
If set to a positive number, the total number of training steps to perform. Overrides
num_train_epochs. In case of using a finite iterable dataset the training may stop before reaching
the set number of steps when all data is exhausted.   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) —
Number of steps used for a linear warmup from 0 to learning_rate. Overrides any effect of
warmup_ratio.    A method that regroups all arguments linked to the learning rate scheduler and its hyperparameters.  Example:   Copied >>> from transfo


TrainingArguments

rmers import TrainingArguments
>>> args = TrainingArguments("working_dir")
>>> args = args.set_lr_scheduler(name="cosine", warmup_ratio=0.05)
>>> args.warmup_ratio
0.05  set_optimizer  < source > ( name: typing.Union[str, transformers.training_args.OptimizerNames] = 'adamw_torch' learning_rate: float = 5e-05 weight_decay: float = 0 beta1: float = 0.9 beta2: float = 0.999 epsilon: float = 1e-08 args: typing.Optional[str] = None  )   Parameters   name (str or training_args.OptimizerNames, optional, defaults to "adamw_torch") —
The optimizer to use: "adamw_hf", "adamw_torch", "adamw_torch_fused", "adamw_apex_fused",
"adamw_anyprecision" or "adafactor".   learning_rate (float, optional, defaults to 5e-5) —
The initial learning rate.   weight_decay (float, optional, defaults to 0) —
The weight decay to apply (if not zero) to all layers except all bias and LayerNorm weights.   beta1 (float, optional, defaults to 0.9) —
The beta1 hyperparameter for the adam optimizer or its var


TrainingArguments

iants.   beta2 (float, optional, defaults to 0.999) —
The beta2 hyperparameter for the adam optimizer or its variants.   epsilon (float, optional, defaults to 1e-8) —
The epsilon hyperparameter for the adam optimizer or its variants.   args (str, optional) —
Optional arguments that are supplied to AnyPrecisionAdamW (only useful when
optim="adamw_anyprecision").    A method that regroups all arguments linked to the optimizer and its hyperparameters.  Example:   Copied >>> from transformers import TrainingArguments
>>> args = TrainingArguments("working_dir")
>>> args = args.set_optimizer(name="adamw_torch", beta1=0.8)
>>> args.optim
'adamw_torch'  set_push_to_hub  < source > ( model_id: str strategy: typing.Union[str, transformers.trainer_utils.HubStrategy] = 'every_save' token: typing.Optional[str] = None private_repo: bool = False always_push: bool = False  )   Parameters   model_id (str) —
The name of the repository to keep in sync with the local output_dir. It can be a


TrainingArguments

 simple model ID in
which case the model will be pushed in your namespace. Otherwise it should be the whole repository
name, for instance "user_name/model", which allows you to push to an organization you are a member of
with "organization_name/model".   strategy (str or HubStrategy, optional, defaults to "every_save") —
Defines the scope of what is pushed to the Hub and when. Possible values are:
"end": push the model, its configuration, the tokenizer (if passed along to the Trainer) and a
draft of a model card when the save_model() method is called.
"every_save": push the model, its configuration, the tokenizer (if passed along to the Trainer)
and
a draft of a model card each time there is a model save. The pushes are asynchronous to not block
training, and in case the save are very frequent, a new push is only attempted if the previous one is
finished. A last push is made with the final model at the end of training.
"checkpoint": like "every_save" but the latest check