trainer = Trainer( # Function that returns the model to train. It's useful to use a function # instead of directly the model to make sure that we are always training # an untrained model from scratch. model_init=model_init, # The training arguments. args=args, # The training dataset. ...
# 每隔10步记录一次日志 logging_first_step=True,# 在第一步就记录日志 logging_epoch_end=True,# 在每个 epoch 结束时记录日志)trainer=Trainer(model=model,# 要训练的模型 args=training_args,# 训练参数 train_dataset=train_dataset,# 训练数据集 eval_dataset=eval_dataset # 评估数据集)trainer.train()...
logging_steps=25, report_to=["tensorboard"], load_best_model_at_end=True, metric_for_best_model="wer", greater_is_better=False, push_to_hub=True, ) 开始训练: from transformers import Seq2SeqTrainer trainer = Seq2SeqTrainer( args=training_args, model=model, train_dataset=common_voice["...
training_args=TrainingArguments(per_device_train_batch_size=1,gradient_accumulation_steps=4,gradient_checkpointing=True,**default_args)trainer=Trainer(model=model,args=training_args,train_dataset=ds)result=trainer.train()print_summary(result) 输出结果: GPU Memory进一步降低 (4169MB --> 3706MB), 吞...
report_to为模型训练、评估中的重要指标(如loss, accurace)输出之处,可选择azure_ml, clearml, codecarbon, comet_ml, dagshub, flyte, mlflow, neptune, tensorboard, wandb,使用all会输出到所有的地方,使用no则不会输出。 下面我们使用Trainer进行BERT模型微调,给出英语、中文数据集上文本分类的示例代码。 BERT微...
per_device_train_batch_size (int, optional, defaults to 8) – The batch size per GPU/TPU core/CPU for training. trainer默认自动开启torch的多gpu模式,这里是设置每个gpu上的样本数量,一般来说,多gpu模式希望多个gpu的性能尽量接近,否则最终多gpu的速度由最慢的gpu决定,比如快gpu 跑一个batch需要5秒,跑...
System Info I'm trying to train T5 model using HugggingFace trainer, but I keep getting this error during the evaluation: TypeError: argument 'ids': 'list' object cannot be interpreted as an integer This is the code for the training argu...
model: model可以是一个集成了 transformers.PreTrainedMode 或者torch.nn.module的模型,官方提到trainer对 transformers.PreTrainedModel进行了优化,建议使用。transformers.PreTrainedModel,用于可以通过自己继承这个父类来实现huggingface的model自定义,自定义的过程和torch非常相似,这部分放到huggingface的自定义里讲。
report_to="tensorboard" ) trainer = SFTTrainer( model=model, train_dataset=dataset, peft_config=peft_config, # use our lora peft config dataset_text_field="text", max_seq_length=4096, # no max sequence length tokenizer=tokenizer, # use the llama tokenizer ...
Fix trainer test wrt DeepSpeed + auto_find_bs by @muellerzr in #29061 Add chat support to text generation pipeline by @Rocketknight1 in #28945 [Docs] Spanish translation of task_summary.md by @aaronjimv in #28844 [Awq] Add peft support for AWQ by @younesbelkada in #28987 ...