Update README.md
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README.md
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@@ -61,21 +61,22 @@ Can be fine-tuned further for specific databases or Arabic dialect adaptations.
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- Ensure compatibility with specific database schemas.
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## How to Get Started with the Model
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-
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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device = "cuda" if torch.cuda.is_available() else "cpu"
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base_model_id = "Qwen/Qwen2.5-1.5B-Instruct"
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finetuned_model_id = "OsamaMo/Arabic_Text-To-SQL_using_Qwen2.5-1.5B"
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model = AutoModelForCausalLM.from_pretrained(
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base_model_id,
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device_map="auto",
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torch_dtype=torch.bfloat16
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)
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-
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model.load_adapter(finetuned_model_id)
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tokenizer = AutoTokenizer.from_pretrained(base_model_id)
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@@ -86,25 +87,150 @@ def generate_resp(messages):
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tokenize=False,
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add_generation_prompt=True
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)
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-
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model_inputs = tokenizer([text], return_tensors="pt").to(device)
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-
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generated_ids = model.generate(
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model_inputs.input_ids,
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max_new_tokens=1024,
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-
do_sample=False,
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)
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-
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generated_ids = [
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output_ids[len(input_ids):]
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for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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-
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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-
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return response
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```
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## Training Details
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### Training Data
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- Ensure compatibility with specific database schemas.
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## How to Get Started with the Model
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+
### Load Model
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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+
import re
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device = "cuda" if torch.cuda.is_available() else "cpu"
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base_model_id = "Qwen/Qwen2.5-1.5B-Instruct"
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finetuned_model_id = "OsamaMo/Arabic_Text-To-SQL_using_Qwen2.5-1.5B"
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# Load the base model and adapter for fine-tuning
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model = AutoModelForCausalLM.from_pretrained(
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base_model_id,
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device_map="auto",
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torch_dtype=torch.bfloat16
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)
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model.load_adapter(finetuned_model_id)
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tokenizer = AutoTokenizer.from_pretrained(base_model_id)
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(device)
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generated_ids = model.generate(
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model_inputs.input_ids,
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max_new_tokens=1024,
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do_sample=False, temperature= False,
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)
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generated_ids = [
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output_ids[len(input_ids):]
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for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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return response
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```
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+
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+
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### Example Usage
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```python
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# Production-ready system message for SQL generation
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system_message = (
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"You are a highly advanced Arabic text-to-SQL converter. Your mission is to Understand first the db schema and reltions between it and then accurately transform Arabic "
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"natural language queries into SQL queries with precision and clarity.\n"
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)
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def get_sql_query(db_schema, arabic_query):
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# Construct the instruction message including the DB schema and the Arabic query
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instruction_message = "\n".join([
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"## DB-Schema:",
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db_schema,
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"",
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"## User-Prompt:",
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arabic_query,
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"# Output SQL:",
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"```SQL"
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])
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messages = [
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{"role": "system", "content": system_message},
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{"role": "user", "content": instruction_message}
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]
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response = generate_resp(messages)
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# Extract the SQL query from the response using a regex to capture text within the ```sql markdown block
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match = re.search(r"```sql\s*(.*?)\s*```", response, re.DOTALL | re.IGNORECASE)
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if match:
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sql_query = match.group(1).strip()
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return sql_query
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else:
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return response.strip()
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# Example usage:
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example_db_schema = r'''{
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'Pharmcy':
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CREATE TABLE `purchase` (
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`BARCODE` varchar(20) NOT NULL,
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`NAME` varchar(50) NOT NULL,
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`TYPE` varchar(20) NOT NULL,
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`COMPANY_NAME` varchar(20) NOT NULL,
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`QUANTITY` int NOT NULL,
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`PRICE` double NOT NULL,
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`AMOUNT` double NOT NULL,
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PRIMARY KEY (`BARCODE`),
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KEY `fkr3` (`COMPANY_NAME`),
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CONSTRAINT `fkr3` FOREIGN KEY (`COMPANY_NAME`) REFERENCES `company` (`NAME`) ON DELETE CASCADE ON UPDATE CASCADE
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) ENGINE=InnoDB DEFAULT CHARSET=latin1
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CREATE TABLE `sales` (
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`BARCODE` varchar(20) NOT NULL,
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`NAME` varchar(50) NOT NULL,
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`TYPE` varchar(10) NOT NULL,
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`DOSE` varchar(10) NOT NULL,
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`QUANTITY` int NOT NULL,
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`PRICE` double NOT NULL,
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`AMOUNT` double NOT NULL,
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`DATE` varchar(15) NOT NULL
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) ENGINE=InnoDB DEFAULT CHARSET=latin1
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CREATE TABLE `users` (
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`ID` int NOT NULL,
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`NAME` varchar(50) NOT NULL,
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`DOB` varchar(20) NOT NULL,
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`ADDRESS` varchar(100) NOT NULL,
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`PHONE` varchar(20) NOT NULL,
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`SALARY` double NOT NULL,
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`PASSWORD` varchar(20) NOT NULL,
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PRIMARY KEY (`ID`)
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) ENGINE=InnoDB DEFAULT CHARSET=latin1
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CREATE TABLE `history_sales` (
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`USER_NAME` varchar(20) NOT NULL,
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`BARCODE` varchar(20) NOT NULL,
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`NAME` varchar(50) NOT NULL,
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`TYPE` varchar(10) NOT NULL,
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`DOSE` varchar(10) NOT NULL,
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`QUANTITY` int NOT NULL,
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`PRICE` double NOT NULL,
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`AMOUNT` double NOT NULL,
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`DATE` varchar(15) NOT NULL,
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`TIME` varchar(20) NOT NULL
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) ENGINE=InnoDB DEFAULT CHARSET=latin1
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CREATE TABLE `expiry` (
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`PRODUCT_NAME` varchar(50) NOT NULL,
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`PRODUCT_CODE` varchar(20) NOT NULL,
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`DATE_OF_EXPIRY` varchar(10) NOT NULL,
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`QUANTITY_REMAIN` int NOT NULL
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) ENGINE=InnoDB DEFAULT CHARSET=latin1
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CREATE TABLE `drugs` (
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`NAME` varchar(50) NOT NULL,
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`TYPE` varchar(20) NOT NULL,
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`BARCODE` varchar(20) NOT NULL,
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`DOSE` varchar(10) NOT NULL,
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`CODE` varchar(10) NOT NULL,
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`COST_PRICE` double NOT NULL,
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`SELLING_PRICE` double NOT NULL,
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`EXPIRY` varchar(20) NOT NULL,
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`COMPANY_NAME` varchar(50) NOT NULL,
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`PRODUCTION_DATE` date NOT NULL,
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`EXPIRATION_DATE` date NOT NULL,
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`PLACE` varchar(20) NOT NULL,
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`QUANTITY` int NOT NULL,
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PRIMARY KEY (`BARCODE`)
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) ENGINE=InnoDB DEFAULT CHARSET=latin1
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CREATE TABLE `company` (
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`NAME` varchar(50) NOT NULL,
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`ADDRESS` varchar(50) NOT NULL,
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`PHONE` varchar(20) NOT NULL,
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PRIMARY KEY (`NAME`)
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) ENGINE=InnoDB DEFAULT CHARSET=latin1
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Answer the following questions about this schema:
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}'''
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example_arabic_query = "اريد الباركود الخاص بدواء يبداء اسمه بحرف 's'"
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sql_result = get_sql_query(example_db_schema, example_arabic_query)
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print("استعلام SQL الناتج:")
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print(sql_result)
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```
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## Training Details
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### Training Data
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