Update README.md

README.md

@@ -29,7 +29,7 @@ pipeline_tag: text-generation
 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
+```markdown
 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
@@ -40,16 +40,6 @@ The tasks that the model can accomplish are the following.
 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
 
@@ -60,105 +50,182 @@ The model is open source under apache 2.0. License
 
 ```bash
 pip install transformers
-
+pip install accelerate
 ```
 
 ### Prompt
 ```python
-prompt = f"""<schema>{schema}</schema>
-<question>{question}</question>
-<sql>"""
+prompt = f"""<question>{}</question>
+<doc/code/any tag that explains the task at hand>"""
 ```
 
 ### 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])
+from accelerate import Accelerator
+import torch
+path = "PipableAI/pip-api-expert"
+model =AutoModelForCausalLM.from_pretrained(path,torch_dtype=torch.bfloat16,device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained(path)
+prompt = "<question>Perform api call to do task k</question><python_code>"
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_new_tokens=1200)
+doc = (
+    tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True)
+)
+print(doc)
 ```
 
 
 ## 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);
+### markdown documentation
+```markdown
+swagger_docs = """
+Method access
+HTTP
+JavaScript
+Python
+Java
+POST
+https://slack.com/api/chat.postMessage
+Required scopes
+Bot tokens
+chat:write
+User tokens
+chat:write
+chat:write:user
+chat:write:bot
+Legacy bot tokens
+bot
+Content types
+application/x-www-form-urlencoded
+application/json
+Rate limits
+Special
+Arguments
+Required arguments
+token
+token
+·Required
+Authentication token bearing required scopes. Tokens should be passed as an HTTP Authorization header or alternatively, as a POST parameter.
+Example
+xxxx-xxxxxxxxx-xxxx
+channel
+string
+·Required
+Channel, private group, or IM channel to send message to. Can be an encoded ID, or a name. See below for more details.
+Example
+C1234567890
+At least one of
+attachmentsblockstext
+One of these arguments is required to describe the content of the message. If attachments or blocks are included, text will be used as fallback text for notifications only.
+attachments
+string
+blocks
+blocks[] as string
+text
+string
+How this field works and whether it is required depends on other fields you use in your API call. See below for more detail.
+Example
+Hello world
+Optional arguments
+as_user
+boolean
+·Optional
+(Legacy) Pass true to post the message as the authed user instead of as a bot. Defaults to false. Can only be used by classic Slack apps. See authorship below.
+Example
+true
+icon_emoji
+string
+·Optional
+Emoji to use as the icon for this message. Overrides icon_url.
+Example
+:chart_with_upwards_trend:
+icon_url
+string
+·Optional
+URL to an image to use as the icon for this message.
+Example
+http://lorempixel.com/48/48
+link_names
+boolean
+·Optional
+Find and link user groups. No longer supports linking individual users; use syntax shown in Mentioning Users instead.
+Example
+true
+metadata
+string
+·Optional
+JSON object with event_type and event_payload fields, presented as a URL-encoded string. Metadata you post to Slack is accessible to any app or user who is a member of that workspace.
+Example
+{"event_type": "task_created", "event_payload": { "id": "11223", "title": "Redesign Homepage"}}
+mrkdwn
+boolean
+·Optional
+Disable Slack markup parsing by setting to false. Enabled by default.
+Default
+true
+Example
+false
+parse
+string
+·Optional
+Change how messages are treated. See below.
+Example
+full
+reply_broadcast
+boolean
+·Optional
+Used in conjunction with thread_ts and indicates whether reply should be made visible to everyone in the channel or conversation. Defaults to false.
+Example
+true
+thread_ts
+string
+·Optional
+Provide another message's ts value to make this message a reply. Avoid using a reply's ts value; use its parent instead.
+unfurl_links
+boolean
+·Optional
+Pass true to enable unfurling of primarily text-based content.
+Example
+true
+unfurl_media
+boolean
+·Optional
+Pass false to disable unfurling of media content.
+Example
+false
+username
+string
+·Optional
+Set your bot's user name.
+Example
+My Bot
+"""
+
+question = """
+Convert the above docs to markdown format.
+"""
+
+prompt = f"""
+<api_doc>
+{swagger_docs}
+</api_doc>
+<question>
+{question}
+</question>
+<response>
+"""
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_new_tokens=1800)
+doc = (
+    tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True)
+)
+print(doc)
 ```
 
-### 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