update README

README.md

@@ -1,3 +1,70 @@
 ---
 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
+```