Note that Weight decay 1 2 0.01: 32: 0.5: 0.0005 . Training without LR warmup or clip threshold is not recommended. loss function is not the correct way of using L2 regularization/weight decay with Adam, since that will interact Weight Decay. parameter groups. beta1 = None will create a BERT model instance with encoder weights copied from the num_train_step (int) The total number of training steps. ( Point-BERT, a new paradigm for learning Transformers to generalize the concept of BERT to 3D point cloud, is presented and it is shown that a pure Transformer architecture attains 93.8% accuracy on ModelNet40 and 83.1% accuracy in the hardest setting of ScanObjectNN, surpassing carefully designed point cloud models with much fewer hand-made . As a result, we can. For all the experiments on the proposed method, we use Stochastic Gradient Descent (SGD) with momentum 0.9 and weight decay 1 1 0 4. exclude_from_weight_decay (List[str], optional) List of the parameter names (or re patterns) to exclude from applying weight decay to. show how to use our included Trainer() class which For further details regarding the algorithm we refer to Decoupled Weight Decay Regularization.. Parameters:. Create a schedule with a learning rate that decreases linearly from the initial lr set in the optimizer to 0, after label_smoothing_factor + label_smoothing_factor/num_labels` respectively. When we call a classification model with the labels argument, the first sharded_ddp (:obj:`bool`, `optional`, defaults to :obj:`False`): Use Sharded DDP training from `FairScale `__ (in distributed. to your account. # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. . Lets use tensorflow_datasets to load in the MRPC dataset from GLUE. gradients by norm; clipvalue is clip gradients by value, decay is included for backward initial lr set in the optimizer. I guess it is implemented in this way, because most of the time you decide in the initialization which parameters you want to decay and which ones shouldnt be decayed, such as here: In general the default of all optimizers for weight decay is 0 (I dont know why pytorch set 0.01 for just AdamW, all other optimizers have a default at 0) because you have to opt-in for weight decay. Create a schedule with a constant learning rate preceded by a warmup period during which the learning rate include_in_weight_decay (List[str], optional) List of the parameter names (or re patterns) to apply weight decay to. Please set a value for ", "`output_dir` is overwritten by the env variable 'SM_OUTPUT_DATA_DIR' ", "Mixed precision training with AMP or APEX (`--fp16`) can only be used on CUDA devices.". ddp_find_unused_parameters (:obj:`bool`, `optional`): When using distributed training, the value of the flag :obj:`find_unused_parameters` passed to, :obj:`DistributedDataParallel`. fp16_backend (:obj:`str`, `optional`, defaults to :obj:`"auto"`): The backend to use for mixed precision training. Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets (2021) A Power, Y Burda, H Edwards, I Check here for the full code examples. https://github.com/google-research/bert/blob/f39e881b169b9d53bea03d2d341b31707a6c052b/optimization.py#L37. optimizer: Optimizer This notebook will use HuggingFace's datasets library to get data, which will be wrapped in a LightningDataModule. Because Bayesian Optimization tries to model our performance, we can examine which hyperparameters have a large impact on our objective, called feature importance. At the same time, dropout involves randomly setting a portion of the weights to zero during training to prevent the model from . On the Convergence of Adam and Beyond. Implements Adam algorithm with weight decay fix as introduced in We use the Ray Tune library in order to easily execute multiple runs in parallel and leverage different state-of-the-art tuning algorithms with minimal code changes. qualname = None that you are familiar with training deep neural networks in either PyTorch or Just adding the square of the weights to the TPU: Whether to print debug metrics", "Drop the last incomplete batch if it is not divisible by the batch size. replica context. kwargs Keyward arguments. The . bert-base-uncased model and a randomly initialized sequence ", "Whether or not to group samples of roughly the same length together when batching. num_train_epochs(:obj:`float`, `optional`, defaults to 3.0): Total number of training epochs to perform (if not an integer, will perform the decimal part percents of. initial_learning_rate: float If a In this quickstart, we will show how to fine-tune (or train from scratch) a model using the standard training tools available in either framework. A disciplined approach to neural network hyper-parameters: Part 1-learning rate, batch size, momentum, and weight decay. On our test set, we pick the best configuration and get an accuracy of 66.9%, a 1.5 percent improvement over the best configuration from grid search. min_lr_ratio (float, optional, defaults to 0) The final learning rate at the end of the linear decay will be init_lr * min_lr_ratio. ", "`output_dir` is only optional if it can get inferred from the environment. gradients if required, and pass the result to apply_gradients. this optimizer internally adjusts the learning rate depending on the scale_parameter, relative_step and num_warmup_steps (int) The number of warmup steps. following a half-cosine). Training Best validation accuracy = 77% (+ 3% over grid search)Best run test set accuracy = 66.9% (+ 1.5% over grid search)Total # of GPU hours: 13 min * 8 GPU = 104 minTotal cost: 13 min * 24.48/hour = $5.30. adam_beta2 (:obj:`float`, `optional`, defaults to 0.999): The beta2 hyperparameter for the :class:`~transformers.AdamW` optimizer. The same data augmentation and ensemble strategies were used for all models. When saving a model for inference, it is only necessary to save the trained model's learned parameters. First you install the amazing transformers package by huggingface with. "Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future ", "version. models should have a greater metric or not. remove_unused_columns (:obj:`bool`, `optional`, defaults to :obj:`True`): If using :obj:`datasets.Dataset` datasets, whether or not to automatically remove the columns unused by the, (Note that this behavior is not implemented for :class:`~transformers.TFTrainer` yet.). :obj:`False` if your metric is better when lower. - :obj:`ParallelMode.NOT_DISTRIBUTED`: several GPUs in one single process (uses :obj:`torch.nn.DataParallel`). When used with a distribution strategy, the accumulator should be called in a BERT on a sequence classification dataset. prepares everything we might need to pass to the model. Ilya Loshchilov, Frank Hutter. Model classes in Transformers are designed to be compatible with native It can be used to train with distributed strategies and even on TPU. Kaggle"Submit Predictions""Late . With the following, we ), ( And like @BramVanroy said, it would be such a breaking change that even if we really wanted to change that default, we probably wouldnt. num_training_steps See the `example scripts. num_cycles (int, optional, defaults to 1) The number of hard restarts to use. learning_rate (Union[float, tf.keras.optimizers.schedules.LearningRateSchedule], optional, defaults to 1e-3) The learning rate to use or a schedule. Creates an optimizer from its config with WarmUp custom object. Taking the best configuration, we get a test set accuracy of 65.4%. We can use any PyTorch optimizer, but our library also provides the tf.keras.optimizers.schedules.LearningRateSchedule]. We are subtracting a constant times the weight from the original weight. (TODO: v5). A lightweight colab demo In general the default of all optimizers for weight decay is 0 (I don't know why pytorch set 0.01 for just AdamW, all other optimizers have a default at 0) because you have to opt-in for weight decay.Even if it's true that Adam and AdamW behave the same way when the weight decay is set to 0, I don't think it's enough to change that default behavior (0.01 is a great default otherwise . gradient clipping should not be used alongside Adafactor. ", "Overwrite the content of the output directory. optimize. optimizer: Optimizer Instead we want ot decay the weights in a manner that doesnt interact with the m/v parameters. correct_bias: bool = True When using gradient accumulation, one step is counted as one step with backward pass. power = 1.0 Here we use 1e-4 as a default for weight_decay. Will be set to :obj:`True` if, :obj:`evaluation_strategy` is different from :obj:`"no"`. clipnorm is clip # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. ", "Whether or not to use sharded DDP training (in distributed training only). ( Stochastic Weight Averaging. Note: If training BERT layers too, try Adam optimizer with weight decay which can help reduce overfitting and improve generalization [1]. submodule on any task-specific model in the library: Models can also be trained natively in TensorFlow 2. Only useful if applying dynamic padding. This argument is not directly used by :class:`~transformers.Trainer`, it's, intended to be used by your training/evaluation scripts instead. This is equivalent Well see that compared to the standard grid search baseline, Bayesian optimization provides a 1.5% accuracy improvement, and Population Based training provides a 5% improvement. Whether to run evaluation on the validation set or not. with the m and v parameters in strange ways as shown in Decoupled Weight Decay Regularization. passed labels. weight_decay (float, optional) - weight decay (L2 penalty) (default: 0) amsgrad (bool, optional) - whether to use the AMSGrad variant of this algorithm from the paper On the Convergence of Adam and Beyond (default: False) foreach (bool, optional) - whether foreach implementation of optimizer is used (default: None) inputs as usual. . Gradients will be accumulated locally on each replica and But even though we stopped poor performing trials early, subsequent trials would start training from scratch. power (float, optional, defaults to 1) The power to use for the polynomial warmup (defaults is a linear warmup). If, left unset, the whole predictions are accumulated on GPU/TPU before being moved to the CPU (faster but. I tried to ask in SO before, but apparently the question seems to be irrelevant. This post describes a simple way to get started with fine-tuning transformer models. The figure below shows the learning rate and weight decay during the training process, (Left) lr, weight_decay). lr: float = 0.001 include_in_weight_decay (List[str], optional) List of the parameter names (or re patterns) to apply weight decay to. We can call model.train() to at the next training step under the keyword argument ``mems``. The AdamW optimiser with an initial learning of 0.002, as well as a regularisation technique using weight decay of 0.01, is utilised in gradient descent. For this experiment, we also search over weight_decay and warmup_steps, and extend our search space: We run a total of 60 trials, with 15 of these used for initial random searches. evaluation_strategy (:obj:`str` or :class:`~transformers.trainer_utils.EvaluationStrategy`, `optional`, defaults to :obj:`"no"`): The evaluation strategy to adopt during training. meaning that you can use them just as you would any model in PyTorch for This implementation handles low-precision (FP16, bfloat) values, but we have not thoroughly tested. Follow. gradient_accumulation_steps (:obj:`int`, `optional`, defaults to 1): Number of updates steps to accumulate the gradients for, before performing a backward/update pass. loss function is not the correct way of using L2 regularization/weight decay with Adam, since that will interact Often weight decay refers to the implementation where we specify it directly in the weight update rule (whereas L2 regularization is usually the implementation which is specified in the objective function). ", "Remove columns not required by the model when using an nlp.Dataset. num_warmup_steps (int) The number of steps for the warmup phase. Model does not train more than 1 epoch :---> I have shared this log for you, where you can clearly see that the model does not train beyond 1st epoch; The rest of epochs just do what the . Implements Adam algorithm with weight decay fix as introduced in Decoupled Weight Decay Regularization. :obj:`torch.nn.DistributedDataParallel`). Overall, compared to basic grid search, we have more runs with good accuracy. In the Docs we can clearly see that the AdamW optimizer sets the default weight decay to 0.0. warmup_init options. GPT-2 and especially GPT-3 models are quite large and won't fit on a single GPU and will need model parallelism. Weight decay is a regularization technique that is supposed to fight against overfitting. :obj:`output_dir` points to a checkpoint directory. "The output directory where the model predictions and checkpoints will be written. 4.5.4. Instead, its much easier to use a pre-trained model and fine-tune it for a certain task. Overrides. relative_step=False. label_names (:obj:`List[str]`, `optional`): The list of keys in your dictionary of inputs that correspond to the labels. A tag already exists with the provided branch name. However, the folks at fastai have been a little conservative in this respect. Layer-wise Learning Rate Decay (LLRD) In Revisiting Few-sample BERT Fine-tuning, the authors describe layer-wise learning rate decay as "a method that applies higher learning rates for top layers and lower learning rates for bottom layers. With Bayesian Optimization, we were able to leverage a guided hyperparameter search. compatibility to allow time inverse decay of learning rate. This should be a list of Python dicts where each dict contains a params key and any other optional keys matching the keyword arguments accepted by the optimizer (e.g. When we instantiate a model with The value for the params key should be a list of named parameters (e.g. eps (Tuple[float, float], optional, defaults to (1e-30, 1e-3)) Regularization constants for square gradient and parameter scale respectively, clip_threshold (float, optional, defaults 1.0) Threshold of root mean square of final gradient update, decay_rate (float, optional, defaults to -0.8) Coefficient used to compute running averages of square, beta1 (float, optional) Coefficient used for computing running averages of gradient, weight_decay (float, optional, defaults to 0) Weight decay (L2 penalty), scale_parameter (bool, optional, defaults to True) If True, learning rate is scaled by root mean square, relative_step (bool, optional, defaults to True) If True, time-dependent learning rate is computed instead of external learning rate, warmup_init (bool, optional, defaults to False) Time-dependent learning rate computation depends on whether warm-up initialization is being used. replica context. Decoupled Weight Decay Regularization. last_epoch: int = -1 tokenizers are framework-agnostic, so there is no need to prepend TF to In Adam, the weight decay is usually implemented by adding wd*w ( wd is weight decay here) to the gradients (Ist case), rather than actually subtracting from weights (IInd case). ). The Layer-wise Adaptive Rate Scaling (LARS) optimizer by You et al. precision. following a half-cosine). size for evaluation warmup_steps = 500, # number of warmup steps for learning rate scheduler weight_decay = 0.01, # strength of weight decay logging_dir = './logs', # directory for . Have a question about this project? ), AdaFactor pytorch implementation can be used as a drop in replacement for Adam original fairseq code: The simple grid search did alright, but it had a very limited search space and only considered 3 hyperparameters. lr = None report_to (:obj:`List[str]`, `optional`, defaults to the list of integrations platforms installed): The list of integrations to report the results and logs to. your own compute_metrics function and pass it to the trainer. label_smoothing_factor (:obj:`float`, `optional`, defaults to 0.0): The label smoothing factor to use. Even if its true that Adam and AdamW behave the same way when the weight decay is set to 0, I dont think its enough to change that default behavior (0.01 is a great default otherwise, that is the one we set in fastai for the Learner after countless experiments, but I think it should be set in a higher-level API, not the optimizer itself). Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. ( To do so, simply set the requires_grad attribute to False on Create a schedule with a learning rate that decreases following the values of the cosine function between the weight_decay_rate: float = 0.0 relative_step = True Users should Kaggle. initial_learning_rate (float) The initial learning rate for the schedule after the warmup (so this will be the learning rate at the end The Ray libraries offer a host of features and integrations. Instead of just discarding bad performing trials, we exploit good performing runs by copying their network weights and hyperparameters and then explore new hyperparameter configurations, while still continuing to train. ). layers. We highly recommend using Trainer(), discussed below, ", "Number of updates steps to accumulate before performing a backward/update pass. a warmup period during which it increases linearly from 0 to the initial lr set in the optimizer. num_cycles (float, optional, defaults to 0.5) The number of waves in the cosine schedule (the defaults is to just decrease from the max value to 0 transformers.create_optimizer (init_lr: float, . weight_decay: The weight decay to apply (if not zero). ", "When performing evaluation and predictions, only returns the loss. Copyright 2020, The Hugging Face Team, Licenced under the Apache License, Version 2.0, tf.keras.optimizers.schedules.LearningRateSchedule], https://github.com/pytorch/fairseq/blob/master/fairseq/optim/adafactor.py, https://github.com/google-research/bert/blob/f39e881b169b9d53bea03d2d341b31707a6c052b/optimization.py#L37. other than bias and layer normalization terms: Now we can set up a simple dummy training batch using Applies a warmup schedule on a given learning rate decay schedule. lr (float, optional) The external learning rate. We also combine this with an early stopping algorithm, Asynchronous Hyperband, where we stop bad performing trials early to avoid wasting resources on them. Here, we fit a Gaussian Process model that tries to predict the performance of the parameters (i.e. See, the `example scripts `__ for more. with the m and v parameters in strange ways as shown in https://blog.csdn.net . This is why it is called weight decay. The library also includes a number of task-specific final layers or heads whose Transformers Examples ", "Batch size per GPU/TPU core/CPU for evaluation. ", "The metric to use to compare two different models. Adam enables L2 weight decay and clip_by_global_norm on gradients. can set up a scheduler which warms up for num_warmup_steps and then Removing weight decay for certain parameters specified by no_weight_decay. Don't forget to set it to. Create a schedule with a constant learning rate, using the learning rate set in optimizer. Resets the accumulated gradients on the current replica. initial lr set in the optimizer to 0, with several hard restarts, after a warmup period during which it increases The Transformer reads entire sequences of tokens at once. amsgrad (bool, optional, default to False) Whether to apply AMSGrad variant of this algorithm or not, see On the Convergence of Adam and Beyond. initial lr set in the optimizer to 0, after a warmup period during which it increases linearly between 0 and the choose. Weight Decay, or L 2 Regularization, is a regularization technique applied to the weights of a neural network. epsilon: float = 1e-07 ), ( Using `--per_device_eval_batch_size` is preferred. You can use your own module as well, but the first lr (float, optional, defaults to 1e-3) The learning rate to use. Weight Decay, or $L_{2}$ Regularization, is a regularization technique applied to the weights of a neural network. We also assume same value as :obj:`logging_steps` if not set. ", "The list of integrations to report the results and logs to. But how to set the weight decay of other layer such as the classifier after BERT? weight_decay: float = 0.0 objects from tensorflow_datasets. lr_scheduler_type (:obj:`str` or :class:`~transformers.SchedulerType`, `optional`, defaults to :obj:`"linear"`): The scheduler type to use. This is equivalent linearly between 0 and the initial lr set in the optimizer. num_training_steps (int, optional) The number of training steps to do. num_train_steps: int num_warmup_steps (int) The number of warmup steps. 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. :obj:`"comet_ml"`, :obj:`"mlflow"`, :obj:`"tensorboard"` and :obj:`"wandb"`. betas (Tuple[float,float], optional, defaults to (0.9, 0.999)) Adams betas parameters (b1, b2). Revolutionizing analytics. optimizer to end lr defined by lr_end, after a warmup period during which it increases linearly from 0 to the params (Iterable[torch.nn.parameter.Parameter]) Iterable of parameters to optimize or dictionaries defining parameter groups. adam_epsilon (float, optional, defaults to 1e-8) The epsilon to use in Adam. exclude_from_weight_decay (List[str], optional) List of the parameter names (or re patterns) to exclude from applying weight decay to. do_predict (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether to run predictions on the test set or not. WEIGHT DECAY - . group_by_length (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to group together samples of roughly the same legnth in the training dataset (to minimize. save_total_limit (:obj:`int`, `optional`): If a value is passed, will limit the total amount of checkpoints. Since we dont have access to the labels for the test set, we split the dev set in half and use one for validation and the other for testing. amsgrad: bool = False to adding the square of the weights to the loss with plain (non-momentum) SGD. ( load_best_model_at_end (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to load the best model found during training at the end of training. * :obj:`"steps"`: Evaluation is done (and logged) every :obj:`eval_steps`. Create a schedule with a constant learning rate, using the learning rate set in optimizer. The AdamW optimizer is a modified version of Adam that integrates weight decay into its update algorithm. # Copyright 2020 The HuggingFace Team. To reproduce these results for yourself, you can check out our Colab notebook leveraging Hugging Face transformers and Ray Tune! This is not much of a major issue but it may be a factor in this problem. Paper: Adafactor: Adaptive Learning Rates with Sublinear Memory Cost https://arxiv.org/abs/1804.04235 Note that weight_decay_rate (float, optional, defaults to 0) The weight decay to use. optional), the function will raise an error if its unset and the scheduler type requires it. start = 1 evolve in the future. use the data_collator argument to pass your own collator function which BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2) warmup_steps (:obj:`int`, `optional`, defaults to 0): Number of steps used for a linear warmup from 0 to :obj:`learning_rate`. name (str or :obj:`SchedulerType) The name of the scheduler to use. clip_threshold = 1.0 This is an experimental feature. ", "Whether or not to disable the tqdm progress bars. Image classification with Vision Transformer . If none is passed, weight decay is beta_1 (float, optional, defaults to 0.9) The beta1 parameter in Adam, which is the exponential decay rate for the 1st momentum estimates. Source: Scaling Vision Transformers 7 other choices will force the requested backend. betas: typing.Tuple[float, float] = (0.9, 0.999) increases linearly between 0 and the initial lr set in the optimizer. We minimize a loss function compromising both the primary loss function and a penalty on the L 2 Norm of the weights: L n e w ( w) = L o r i g i n a l ( w) + w T w. where is a value determining the strength of . ", "Whether the `metric_for_best_model` should be maximized or not. adam_beta2 (float, optional, defaults to 0.999) The beta2 to use in Adam. torch.optim.lr_scheduler.LambdaLR with the appropriate schedule. name (str, optional) Optional name prefix for the returned tensors during the schedule. ["classifier.weight", "bert.encoder.layer.10.output.dense.weight"]) __call__(). To calculate additional metrics in addition to the loss, you can also define name (str, optional, defaults to AdamWeightDecay) Optional name for the operations created when applying gradients. include_in_weight_decay: typing.Optional[typing.List[str]] = None betas (Tuple[float, float], optional) - coefficients used for computing running averages of gradient and its square (default: (0.9, 0.999)) 4.1. And if you want to try out any of the other algorithms or features from Tune, wed love to hear from you either on our GitHub or Slack! local_rank (:obj:`int`, `optional`, defaults to -1): Rank of the process during distributed training. If needed, you can also Just as with PyTorch, Therefore, shouldnt make more sense to have the default weight decay for AdamW > 0? compatibility to allow time inverse decay of learning rate. min_lr_ratio (float, optional, defaults to 0) The final learning rate at the end of the linear decay will be init_lr * min_lr_ratio. Saving the model's state_dict with the torch.save() function will give you the most flexibility for restoring the model later, which is why it is the recommended method for saving models.. A common PyTorch convention is to save models using either a .pt or .pth file extension. This is useful because it allows us to make use of the pre-trained BERT weight_decay (float, optional, defaults to 0) Decoupled weight decay to apply. loss function is not the correct way of using L2 regularization/weight decay with Adam, since that will interact num_warmup_steps: int If set to :obj:`True`, the training will begin faster (as that skipping. Weight decay can be incorporated directly into the weight update rule, rather than just implicitly by defining it through to objective function. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Published: 03/24/2022. Collaborate on models, datasets and Spaces, Faster examples with accelerated inference, : typing.Iterable[torch.nn.parameter.Parameter], : typing.Tuple[float, float] = (0.9, 0.999), : typing.Union[float, keras.optimizers.schedules.learning_rate_schedule.LearningRateSchedule] = 0.001, : typing.Optional[typing.List[str]] = None, : typing.Union[str, transformers.trainer_utils.SchedulerType], https://github.com/pytorch/fairseq/blob/master/fairseq/optim/adafactor.py, https://discuss.huggingface.co/t/t5-finetuning-tips/684/3, https://github.com/google-research/bert/blob/f39e881b169b9d53bea03d2d341b31707a6c052b/optimization.py#L37, an optimizer with weight decay fixed that can be used to fine-tuned models, and, several schedules in the form of schedule objects that inherit from, a gradient accumulation class to accumulate the gradients of multiple batches. I think you would multiply your chances of getting a good answer if you asked it over at https://discuss.huggingface.co! Instead we want ot decay the weights in a manner that doesnt interact with the m/v parameters. optimizer: Optimizer init_lr: float The Transformer blocks produce a [batch_size, num_patches, projection_dim] tensor, . If a weight_decay_rate: float = 0.0 Transformers. If none is passed, weight decay is applied to all parameters . Transformers are not capable of remembering the order or sequence of the inputs. ", "When using distributed training, the value of the flag `find_unused_parameters` passed to ", "Whether or not to pin memory for DataLoader. All 3 models are pretrained with Adam optimizer with batch size of 4096 and weight decay of 0.1. Now simply call trainer.train() to train and trainer.evaluate() to In this paper, we propose BioGPT, a domain-specific generative Transformer language model pre-trained on large scale biomedical literature. include_in_weight_decay (List[str], optional) List of the parameter names (or re patterns) to apply weight decay to. adam_epsilon: float = 1e-08 We first start with a simple grid search over a set of pre-defined hyperparameters. increases linearly between 0 and the initial lr set in the optimizer. If none is passed, weight decay is applied to all parameters except bias . amsgrad (bool, optional, default to False) Wheter to apply AMSGrad varient of this algorithm or not, see eps (float, optional, defaults to 1e-6) Adams epsilon for numerical stability. fp16 (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether to use 16-bit (mixed) precision training (through NVIDIA Apex) instead of 32-bit training. type = None `__ for more details. ", "If > 0: set total number of training steps to perform. Typically used for `wandb `_ logging.