如果为True,则在训练过程中会缓存数据,以提高数据加载速度。 logging_dir:这个参数指定日志文件的目录。在训练过程中,会记录训练日志,包括损失、准确率等信息。 logging_steps:这个参数指定每多少步记录一次日志。可以通过调整这个参数来平衡日志输出的频率和内存使用情况。 save_steps:这个参数指定每多少步保存一次模型权
num_train_epochs=3, logging_dir="./logs", # Add more arguments as needed ) In the example above, we create a Seq2SeqTrainingArguments instance with some basic arguments like output_dir, per_device_train_batch_size, num_train_epochs, and logging_dir. You can add more arguments as needed...
❓ Questions & Help Details when I use TrainingArguments (transformer 3,3,1) , it emerge the error TypeError: init() got an unexpected keyword argument 'evaluation_strategy'. I wonder why I 've got this error. these are my code: training_...
Steps to reproduce the behavior: from transformers import TrainingArguments, Trainer, EvalPrediction training_args = TrainingArguments( learning_rate=1e-4, num_train_epochs=6, per_device_train_batch_size=32, per_device_eval_batch_size=32, logging_steps=200, output_dir="./training_output", ove...
from transformers import TrainingArguments training_args = TrainingArguments( output_dir="./results", logging_dir="./logs", learning_rate=2e-5, per_device_train_batch_size=8, per_device_eval_batch_size=8, num_train_epochs=3, weight_decay=0.01, ) 如果evaluation_strategy 是你需要的功能,并...
import logging import os import torch import types import torch.nn.functional as F from megatron.core.models.retro.utils import ( get_config_path as get_retro_config_path, get_gpt_data_dir as get_retro_data_dir, ) from megatron.core.transformer import TransformerConfig from meg...
args = TrainingArguments( output_dir="./checkpoints", per_device_train_batch_size=128, per_device_eval_batch_size=128, evaluation_strategy="steps", eval_steps=1_000, logging_steps=1_000, gradient_accumulation_steps=8, num_train_epochs=50, weight_decay=0.1, warmup_steps=5_000, lr_sched...
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args=TrainingArguments("robs-chesterton-results",evaluation_strategy="epoch",learning_rate=2e-5,per_device_train_batch_size=16,# batch size per device during trainingper_device_eval_batch_size=64,# batch size for evaluationnum_train_epochs=3,weight_decay=0.01,logging_dir='./logs',logging_...
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