---
license: apache-2.0
---

## 简介

这是一款根据自然语言生成 SQL 的模型（NL2SQL/Text2SQL），是我们自研众多 NL2SQL 模型中最为基础的一版，其它高级版模型后续将陆续进行开源。

该模型基于 BART 架构，我们将 NL2SQL 问题建模为类似机器翻译的 Seq2Seq 形式，该模型的优势特点：参数规模较小、但 SQL 生成准确性也较高。

## 用法

NL2SQL 任务中输入参数含有用户查询文本+数据库表信息，目前按照以下格式拼接模型的输入文本：

```
Question: 名人堂一共有多少球员 <sep> Tables: hall_of_fame: player_id, yearid, votedby, ballots, needed, votes, inducted, category, needed_note ; player_award: player_id, award_id, year, league_id, tie, notes <sep>
```

具体使用方法参考以下示例：

```python
import torch
from transformers import AutoModelForSeq2SeqLM, MBartForConditionalGeneration, AutoTokenizer

device = 'cuda'
model_path = 'DMetaSoul/nl2sql-chinese-basic'
sampling = False
tokenizer = AutoTokenizer.from_pretrained(model_path, src_lang='zh_CN')
#model = MBartForConditionalGeneration.from_pretrained(model_path)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)
model = model.half()
model.to(device)


input_texts = [
    "Question: 所有章节的名称和描述是什么？ <sep> Tables: sections: section id , course id , section name , section description , other details <sep>",
    "Question: 名人堂一共有多少球员 <sep> Tables: hall_of_fame: player_id, yearid, votedby, ballots, needed, votes, inducted, category, needed_note ; player_award: player_id, award_id, year, league_id, tie, notes ; player_award_vote: award_id, year, league_id, player_id, points_won, points_max, votes_first ; salary: year, team_id, league_id, player_id, salary ; player: player_id, birth_year, birth_month, birth_day, birth_country, birth_state, birth_city, death_year, death_month, death_day, death_country, death_state, death_city, name_first, name_last, name_given, weight <sep>"
]
inputs = tokenizer(input_texts, max_length=512, return_tensors="pt",
    padding=True, truncation=True)
inputs = {k:v.to(device) for k,v in inputs.items() if k not in ["token_type_ids"]}

with torch.no_grad():
    if sampling:
        outputs = model.generate(**inputs, do_sample=True, top_k=50, top_p=0.95,
            temperature=1.0, num_return_sequences=1, 
            max_length=512, return_dict_in_generate=True, output_scores=True)
    else:
        outputs = model.generate(**inputs, num_beams=4, num_return_sequences=1, 
            max_length=512, return_dict_in_generate=True, output_scores=True)

output_ids = outputs.sequences
results = tokenizer.batch_decode(output_ids, skip_special_tokens=True,
            clean_up_tokenization_spaces=True)

for question, sql in zip(input_texts, results):
    print(question)
    print('SQL: {}'.format(sql))
    print()
```

输入结果如下：

```
Question: 所有章节的名称和描述是什么？ <sep> Tables: sections: section id , course id , section name , section description , other details <sep>
SQL: SELECT section name, section description FROM sections

Question: 名人堂一共有多少球员 <sep> Tables: hall_of_fame: player_id, yearid, votedby, ballots, needed, votes, inducted, category, needed_note ; player_award: player_id, award_id, year, league_id, tie, notes ; player_award_vote: award_id, year, league_id, player_id, points_won, points_max, votes_first ; salary: year, team_id, league_id, player_id, salary ; player: player_id, birth_year, birth_month, birth_day, birth_country, birth_state, birth_city, death_year, death_month, death_day, death_country, death_state, death_city, name_first, name_last, name_given, weight <sep>
SQL: SELECT count(*) FROM hall_of_fame
```