("Trainer.model is not a `PreTrainedModel`, only saving its state dict.") if self.args.save_safetensors: safetensors.torch.save_file(state_dict, os.path.join(output_dir, SAFE_WEIGHTS_NAME)) else: torch.save(state_dict, os.path.join(output_dir, WEIGHTS_NAME)) else: self.model.save...
有了这个最终成分,我们可以使用“Trainer”实例化和微调我们的模型: from transformers import Trainer trainer = Trainer(model=model, args=training_args, compute_metrics=compute_metrics, train_dataset=emotions_encoded["train"], eval_dataset=emotions_encoded["validation"], tokenizer=tokenizer) trainer.train(...
二、Trainer训练类 2.1 概述 2.2 使用示例 代码语言:javascript 复制 from transformersimportAutoModelForSequenceClassification,Trainer,TrainingArguments from datasetsimportload_dataset #1.加载数据集 # 假设我们使用的是Hugging Face的内置数据集,例如SST-2dataset=load_dataset('sst2')# 或者使用你自己的数据集 #2...
trainer = Trainer( model=model, args=training_args, train_dataset=dataset_processed["train"], eval_dataset=dataset_processed["validation"], data_collator=GraphormerDataCollator(),)在用于图分类的Trainer中，对给定的图数据集使用正确的数据整理器 (data collator) 很重要，这个数据整理器会...
from datasets import load_dataset from transformers import AutoTokenizer, DataCollatorForSeq2Seq, AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer """利用load_dataset()来读取数据: - 该方法支持.txt、.csv、.json等文件格式 - 返回结果是一个字典类型 - 读取.txt文件时,若不指定名称,这...
from transformers import Trainer, TrainingArguments, BertForSequenceClassification, BertTokenizer from datasets import load_dataset # 加载数据集 dataset = load_dataset('imdb') # 加载预训练模型和分词器 model = BertForSequenceClassification.from_pretrained('bert-base-uncased') ...
The model itself is a regularPytorchnn.Moduleor aTensorFlowtf.keras.Model(depending on your backend) which you can use as usual.This tutorialexplains how to integrate such a model into a classic PyTorch or TensorFlow training loop, or how to use ourTrainerAPI to quickly fine-tune on a new...
/recall/f1 将数据/模型/参数传入`Trainer`即可""" trainer = Trainer( model, args, train_dataset=tokenized_datasets["train"], eval_dataset=tokenized_datasets["validation"], data_collator=data_collator, tokenizer=tokenizer, compute_metrics=compute_metrics ) """调用`train`方法开始训练""" trainer....
Trainer+文本分类 1.导入相关包 from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments from datasets import load_dataset 1. 2. 2.加载数据集 dataset = load_dataset("csv", data_files="./ChnSentiCorp_htl_all.csv", split="train") dataset = dataset.filt...
运行trainer的train函数后,结果如下所示: 图6.9-运行 train 后的 Trainer 结果 在训练后,必须保存模型和分词器: model.save_pretrained("ner_model")tokenizer.save_pretrained("tokenizer") 如果您希望使用管道(pipeline)使用模型,则必须读取配置文件,并根据label_list对象中使用的标签正确分配label2id和id2label: ...