Transformers训练和微调：Training and Fine-tuning
2024-12-27 22:32
以下是一个例子，展示如何使用TAR：SQL Guided Pre-Training来训练数据：
1.准备数据
首先，需要准备一个包含自然语言问题和对应的SQL查询的数据集。例如，以下是一个简单的数据集：
| 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(revenue) FROM sales WHERE year=2020 |
| Show me the customers who have made at least 3 purchases. | SELECT customer_name FROM sales GROUP BY customer_name HAVING COUNT(*)>=3 |
2.预处理数据
接下来，需要使用TAR：SQL Guided Pre-Training的预处理工具对数据进行处理。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.csv')
dev_data = processor.process(file_path='dev_data.csv')
```
其中，`train_data.csv`和`dev_data.csv`是包含问题和SQL查询的数据集文件。
3.训练模型
接下来，可以使用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='https://blog.csdn.net/qq_42464569/article/details/results',
evaluation_strategy='steps',
eval_steps=100,
save_total_limit=10,
learning_rate=1e-4,
per_device_train_batch_size=2,
per_device_eval_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,
eval_dataset=dev_data,
)
trainer.train()
```
此代码将使用TAR：SQL Guided Pre-Training来训练模型，使用训练数据集`train_data`和开发数据集`dev_data`。其中，`TrainingArguments`是训练参数，可以根据需要进行修改。
4.使用模型
最后，可以使用训练好的模型来进行文本到SQL查询的转换。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')
text = 'What is the name of the employee with ID 123?'
inputs = tokenizer(text, return_tensors='pt')
outputs = model.generate(inputs['input_ids'])
sql_query = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(sql_query)
```