如何在tensorflow中使用transformers保存每个时期的最佳模型

在TensorFlow中使用Transformers保存每个时期的最佳模型，可以通过以下步骤实现：

导入所需的库和模块：

import tensorflow as tf
from transformers import TFAutoModelForSequenceClassification, TFTrainer, TFTrainingArguments

定义模型和训练参数：

model = TFAutoModelForSequenceClassification.from_pretrained('bert-base-uncased')
training_args = TFTrainingArguments(
    output_dir='./results',
    evaluation_strategy='epoch',
    save_strategy='epoch',
    save_total_limit=3,
    logging_dir='./logs',
    logging_steps=10,
    per_device_train_batch_size=16,
    per_device_eval_batch_size=16,
    num_train_epochs=10,
    weight_decay=0.01,
    learning_rate=2e-5,
)

定义训练数据集和评估数据集：

train_dataset = tf.data.Dataset.from_tensor_slices((train_inputs, train_labels)).shuffle(100).batch(16)
eval_dataset = tf.data.Dataset.from_tensor_slices((eval_inputs, eval_labels)).batch(16)

定义评估函数：

def compute_metrics(eval_pred):
    labels = eval_pred.label_ids
    preds = eval_pred.predictions.argmax(axis=-1)
    accuracy = tf.keras.metrics.Accuracy()(labels, preds)
    return {'accuracy': accuracy}

创建Trainer对象并进行训练：

trainer = TFTrainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    compute_metrics=compute_metrics,
)
trainer.train()