设置为True则仅返回损失,注意这个参数比较重要,我们如果要通过trainer的custome metric来自定义模型的eval结果,比如看auc之类的,则这里要设置为False,否则custom metric会被模型忽略而仅仅输出training data的loss。 per_device_train_batch_size (int, optional, defaults to 8) – The batch size per GPU/TPU core...
trainer.data_collator=None#collate_fn ##一定要加 这里一定要注意,trainer默认的data_collator一定要设置为None,这个data_collator是作为collate_fn放到自动转化的dataloader里的(上面代码的train_dataset之类的都是torch的dataset,不需要自己用dataloader封装),会做一些batch处理之类的工作,因为自定义的dataset的输出千差...
import tensorflow as tf def custom_loss(y_true, y_pred): return tf.keras.losses.mean_squared_error(y_true, y_pred) 在微调模型之前,创建一个TFTrainer对象,并将自定义的损失函数传递给该对象的构造函数。代码示例如下: 代码语言:txt 复制 from transformers import TFTrainer t...
I'm following this webpage, trying to load a pandas dataframe into a pytorch dataset in order to use the Trainer API:https://huggingface.co/docs/transformers/training#train-with-pytorch-trainer. My script is shown below. modelName='bert-base-uncased' tokenizer = BertTokenizer.from_pretr...
我正在研究HuggingFaceTransformers,并使用这里的玩具示例:https://huggingface.co/transformers/custom_datasets.html#fine-tuning-with-trainer使用Pytorch训练循环是微不足道的,但使用HuggingFaceTrainer则不明显。num_train_epochs=1, report_to= 浏览81提问于2021-08-12得票数0 ...
huggingface-trainer Hoangdz 186 asked Jul 31 at 4:14 0 votes 1 answer 39 views PanicException: AddedVocabulary bad split AFTER adding tokens to BertTokenizer I use a BertTokenizer and add my custom tokens using add_tokens() function. Minimal sample code here: checkpoint = 'fnlp/bart-ba...
For more flexibility and control over training, TRL provides dedicated trainer classes to post-train language models or PEFT adapters on a custom dataset. Each trainer in TRL is a light wrapper around the 🤗 Transformers trainer and natively supports distributed training methods like DDP, DeepSpeed...
数据集不是pandas数据集,它是pyarrow表,它们有不同的列名,没有loc方法,你需要数据集作为Trainer中的...
在这三个基本类的基础上,该库提供了两个API:pipeline()用于在给定任务上快速使用模型(及其关联的tokenizer和configuration)和 Trainer或者TFtrainer 快速训练或微调给定模型。 因此,该库不是神经网络构建模块的模块化工具箱。如果要扩展/构建库,只需使用常规的Python/ PyTorch / TensorFlow / Keras模块并从库的基类继承...
from transformers import TrainingArguments, Trainer training_args = TrainingArguments( output_dir='pegasus-samsum', num_train_epochs=1, warmup_steps=500, per_device_train_batch_size=1, per_device_eval_batch_size=1, weight_decay=0.01, logging_steps=10, push_to_hub=True, ...