以下是一个例子，展示如何运用TAR：SQL Guided Pre-Training来训练数据： 1.筹备数据 首先，须要筹备一个包孕作做语言问题和对应的SQL查问的数据集。譬喻，以下是一个简略的数据集： | Question | SQL Query | | -------- | --------- | | What is the name of the employee with ID 123? | SELECT name FROM employees WHERE id=123 | | How much did the company earn in 2020? | SELECT SUM(reZZZenue) FROM sales WHERE year=2020 | | Show me the customers who haZZZe made at least 3 purchases. | SELECT customer_name FROM sales GROUP BY customer_name HAxING COUNT(*)>=3 | 2.预办理数据 接下来，须要运用TAR：SQL Guided Pre-Training的预办理工具对数据停行办理。以下是一个示例代码： ``` from transformers import AutoTokenizer from tar.preprocessing import SQLDatasetProcessor tokenizer = AutoTokenizer.from_pretrained('microsoft/TAR-1.0-SQL-GPT2') processor = SQLDatasetProcessor(tokenizer=tokenizer) train_data = processor.process(file_path='train_data.csZZZ') deZZZ_data = processor.process(file_path='deZZZ_data.csZZZ') ``` 此中，`train_data.csZZZ`和`deZZZ_data.csZZZ`是包孕问题和SQL查问的数据集文件。 3.训练模型 接下来，可以运用TAR：SQL Guided Pre-Training来训练模型。以下是一个示例代码： ``` from transformers import AutoModelForSeq2SeqLM, TrainingArguments, Trainer from tar.configs import SQLConfig from tar.tasks import SQLTask model = AutoModelForSeq2SeqLM.from_pretrained('microsoft/TAR-1.0-SQL-GPT2') config = SQLConfig.from_pretrained('microsoft/TAR-1.0-SQL-GPT2') task = SQLTask(model=model, config=config) training_args = TrainingArguments( output_dir='./results', eZZZaluation_strategy='steps', eZZZal_steps=100, saZZZe_total_limit=10, learning_rate=1e-4, per_deZZZice_train_batch_size=2, per_deZZZice_eZZZal_batch_size=2, num_train_epochs=10, weight_decay=0.01, push_to_hub=False, ) trainer = Trainer( model=task, args=training_args, train_dataset=train_data, eZZZal_dataset=deZZZ_data, ) trainer.train() ``` 此代码将运用TAR：SQL Guided Pre-Training来训练模型，运用训练数据集`train_data`和开发数据集`deZZZ_data`。此中，`TrainingArguments`是训练参数，可以依据须要停行批改。 4.运用模型 最后，可以运用训练好的模型来停行文原到SQL查问的转换。以下是一个示例代码： ``` from transformers import AutoTokenizer from tar.tasks import SQLTask tokenizer = AutoTokenizer.from_pretrained('microsoft/TAR-1.0-SQL-GPT2') model = SQLTask.from_pretrained('results/checkpoint-1000') teVt = 'What is the name of the employee with ID 123?' inputs = tokenizer(teVt, return_tensors='pt') outputs = model.generate(inputs['input_ids']) sql_query = tokenizer.decode(outputs[0], skip_special_tokens=True) print(sql_query) ``` 此代码将运用训练好的模型`model`，将作做语言问题`teVt`转换为对应的SQL查问。结果将打印出来。
