TAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing 的使用
时间: 2024-05-25 20:16:38 浏览: 10
TAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing 是一种用于上下文相关文本到SQL解析的预训练方法。该方法旨在通过使用SQL生成器来指导文本到SQL解析器学习SQL生成和解析的技能,从而提高其准确性和鲁棒性。
使用 TAR 方法进行预训练的步骤如下:
1. 收集大量的上下文相关的文本和其对应的SQL查询语句,例如从数据库中抽取的数据集。
2. 利用 SQL 生成器生成与每个上下文相关的 SQL 查询语句。
3. 将文本和生成的 SQL 查询语句作为模型的输入,使用 Transformer 模型进行预训练。在预训练期间,模型学习将文本转换为SQL查询语句的技能。
4. 在预训练之后,使用具有上下文的文本和对应的 SQL 查询语句进行微调,使模型适应特定的任务和数据集。
使用 TAR 方法进行预训练可以提高文本到 SQL 解析器的准确性和鲁棒性,并使其更容易适应不同的数据集和任务。
相关问题
TAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing
TAR (Table-aware Pre-training with Abstract Reasoning) is a pre-training framework for context-dependent text-to-SQL parsing. It leverages SQL knowledge and utilizes abstract reasoning to better understand the context of a natural language query and generate accurate SQL queries.
The TAR model works by first pre-training on a large corpus of text and SQL pairs to learn the general patterns and structures of SQL queries. It then fine-tunes on a smaller dataset of context-dependent text-to-SQL examples to adapt to specific contexts and improve accuracy.
One unique aspect of TAR is its use of table-aware pre-training, which allows the model to incorporate information from the table schema into the pre-training process. This helps the model better understand the relationships between tables and columns, and improves its ability to generate accurate SQL queries.
TAR also incorporates abstract reasoning, which allows the model to make inferences and understand implicit relationships between words and concepts. This helps the model handle more complex queries and improves its overall performance.
Overall, TAR is a promising approach to improving context-dependent text-to-SQL parsing, and has shown strong results on several benchmark datasets.
TAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing 怎么训练数据,给出示例代码
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='./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)
```
此代码将使用训练好的模型`model`,将自然语言问题`text`转换为对应的SQL查询。结果将打印出来。
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