文本生成sql模型（PipableAI/pip-sql-1.3b）

安装环境

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

pip install transformers

代码


question = "What are the email address, town and county of the customers who are of the least common gender?"

schema = """CREATE TABLE Products ( 
  product_id number,
  parent_product_id number,
  product_name text,
  product_price number,
  product_color text,
  product_size text,
  product_description text);

CREATE TABLE Customers (
  customer_id number,
  gender_code text,
  customer_first_name text,
  customer_middle_initial text,
  customer_last_name text,
  email_address text,
  login_name text,
  login_password text,
  phone_number text,
  address_line_1 text,
  town_city text,
  county text,
  country text);

CREATE TABLE Customer_Payment_Methods (
  customer_id number,
  payment_method_code text);

CREATE TABLE Invoices (
  invoice_number number,
  invoice_status_code text,
  invoice_date time);

CREATE TABLE Orders (
  order_id number,
  customer_id number,
  order_status_code text,
  date_order_placed time);

CREATE TABLE Order_Items (
  order_item_id number,
  product_id number,
  order_id number,
  order_item_status_code text);

CREATE TABLE Shipments (
  shipment_id number,
  order_id number,
  invoice_number number,
  shipment_tracking_number text,
  shipment_date time);

CREATE TABLE Shipment_Items (
  shipment_id number,
  order_item_id number);
"""
prompt = f"""<schema>{schema}</schema>
<question>{question}</question>
<sql>"""

from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda"
model = AutoModelForCausalLM.from_pretrained("PipableAI/pip-sql-1.3b")
tokenizer = AutoTokenizer.from_pretrained("PipableAI/pip-sql-1.3b")

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True).split('<sql>')[1].split('</sql>')[0])