Source code for transformers4rec.config.trainer

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from dataclasses import dataclass, field
from typing import Optional

from transformers import TFTrainingArguments, TrainingArguments


[docs]@dataclass class T4RecTrainingArguments(TrainingArguments): """ Class that inherits HF TrainingArguments and add on top of it arguments needed for session-based and sequential-based recommendation Parameters ---------- shuffle_buffer_size: validate_every: Optional[int], int Run validation set every this epoch. -1 means no validation is used by default -1 eval_on_test_set: eval_steps_on_train_set: predict_top_k: Option[int], int Truncate recommendation list to the highest top-K predicted items (do not affect evaluation metrics computation) by default 10 log_predictions : Optional[bool], bool log predictions, labels and metadata features each --compute_metrics_each_n_steps (for test set). by default False log_attention_weights : Optional[bool], bool Logs the inputs and attention weights each --eval_steps (only test set)" by default False learning_rate_num_cosine_cycles_by_epoch : Optional[int], int Number of cycles for by epoch when --lr_scheduler_type = cosine_with_warmup. The number of waves in the cosine schedule (e.g. 0.5 is to just decrease from the max value to 0, following a half-cosine). by default 1.25 experiments_group: Optional[str], str Name of the Experiments Group, for organizing job runs logged on W&B by default "default" """ max_sequence_length: Optional[int] = field( default=None, metadata={"help": "maximum length of sequence"}, ) shuffle_buffer_size: int = field( default=0, metadata={ "help": "Number of samples to keep in the buffer for shuffling." "shuffle_buffer_size=0 means no shuffling" }, ) data_loader_engine: str = field( default="nvtabular", metadata={ "help": "Parquet data loader engine. " "'nvtabular': GPU-accelerated parquet data loader from NVTabular, 'pyarrow': read whole parquet into memory." }, ) eval_on_test_set: bool = field( default=False, metadata={"help": "Evaluate on test set (by default, evaluates on the validation set)."}, ) eval_steps_on_train_set: int = field( default=20, metadata={"help": "Number of steps to evaluate on train set (which is usually large)"}, ) predict_top_k: int = field( default=10, metadata={ "help": "Truncate recommendation list to the highest top-K predicted items (do not affect evaluation metrics computation)" }, ) learning_rate_num_cosine_cycles_by_epoch: float = field( default=1.25, metadata={ "help": "Number of cycles for by epoch when --learning_rate_schedule = cosine_with_warmup." "The number of waves in the cosine schedule " "(e.g. 0.5 is to just decrease from the max value to 0, following a half-cosine)." }, ) log_predictions: bool = field( default=False, metadata={ "help": "Logs predictions, labels and metadata features each --compute_metrics_each_n_steps (for test set)." }, ) compute_metrics_each_n_steps: int = field( default=1, metadata={"help": "Log metrics each n steps (for train, validation and test sets)"}, ) experiments_group: str = field( default="default", metadata={"help": "Name of the Experiments Group, for organizing job runs logged on W&B"}, ) @property def place_model_on_device(self): """ Override the method to allow running training on cpu """ if self.device.type == "cuda": return True return False
[docs]class T4RecTrainingArgumentsTF(T4RecTrainingArguments, TFTrainingArguments): """ Prepare Training arguments for TFTrainer, Inherit arguments from T4RecTrainingArguments and TFTrainingArguments """ pass