PipableAI
/

pip-api-expert

@@ -1,201 +1,164 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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-## Model Details
-### Model Description
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
+license: apache-2.0
+language:
+- en
+metrics:
+- accuracy
+tags:
+- api
+- open-api
+- swagger
+- api doc
+- api call
+- code
+- instruction_tuned
+- basemodel
+- pytorch
+- RL Tuned
+- text-generation-inferenc
 library_name: transformers
+pipeline_tag: text-generation
 ---
+# pip-api-expert
+[pipableAi](https://pipable.ai/)
+[colab_notebook]()
+## What have we built?
+A 1.3 bn state of the art model for api calling , documentation, testing management.
+The tasks that the model can accomplish are the following.
+```javascript
+1.  Convert any bad format text to open api
+2.  Convert any bad format text to mark down.
+3.  Given docs generate and execute the api call in python
+```
+## How we built it?
+We used a simulator and a form of policy gradient to train the model to self instruct itself to make documents and then perform executable calls on the document.
+## Benchmarking :
+For benchmarking purposes we are using Semantic Evaluation for Text-to-SQL with
+Distilled Test Suites, an officially accepted evaluation framework for Spider, SParC, and CoSQL which was proposed by a research team of Yale and Berkeley.
+The benchmark contains 2200 test data points
+Here is the link to run the evaluation:
+## License
+The model is open source under apache 2.0. License
+## Usage
+### Installation
+```bash
+pip install transformers
+```
+### Prompt
+```python
+prompt = f"""<schema>{schema}</schema>
+<question>{question}</question>
+<sql>"""
+```
+### PyTorch
+```python
+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(text, 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])
+```
+## Examples
+### Schema
+```sql
+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);
+```
+### Questions
+What are the email address, town and county of the customers who are of the least common gender?
+```sql
+SELECT email_address ,  town_city ,  county FROM customers GROUP BY gender_code ORDER BY count(*) ASC LIMIT 1
+```
+What are the product price and the product size of the products whose price is above average?
+```sql
+SELECT product_price ,  product_size FROM products WHERE product_price  > (SELECT avg(product_price) FROM products)
+```
+Which customers did not make any orders? List the first name, middle initial and last name.
+```sql
+SELECT T1.customer_first_name ,  T1.customer_middle_initial ,  T1.customer_last_name FROM Customers AS T1 WHERE T1.customer_id NOT IN (SELECT T2.customer_id FROM Orders AS T2)
+```
+### Team
+Avi Kothari, Pratham Gupta, Ritvik Aryan Kalra, Rohan Bhatial, Soham Acharya