modelfun 0.0.1.4

A Python library for the Modelfun API

Please go to MODELFUN to try examples

Eliminate the bottleneck of handmade marks and release AI potential.

Modelfun Python SDK

This package provides functionality developed to simplify interfacing with the [MODELFUN API] in Python 3.

Installation

The package can be installed with pip:

pip install --upgrade modelfun

Install from source:

python setup.py install

Requirements

• Python 3.6+

Quick Start

To use this library, you must have an API key and specify it as a string when creating the modelfun.Client object. API keys can be created through the platform. This is a basic example of the creating the client and using the generate endpoint.

Generate

import modelfun

# initialize the Modelfun Client with an API Key
mo = modelfun.Client('YOUR_API_KEY')
prompt= '''
摘要：
本文总结了十个可穿戴产品的设计原则，而这些原则，同样也是笔者认为是这个行业最吸引人的地方：1.为人们解决重复性问题；2.从人开始，而不是从机器开始；3.要引起注意，但不要刻意；4.提升用户能力，而不是取代人
答案：
'''
# generate a prediction for a prompt 
prediction = mo.generate(
            model_name='modelfun',
            prompt=prompt)
            
# print the predicted text          
print('prediction: {}'.format(prediction.generations[0].text))

Classify

import modelfun
from modelfun.classify import Example
# initialize the Modelfun Client with an API Key
mo = modelfun.Client('YOUR_API_KEY')
response = mo.classify(model_name='modelfun',
  task_name='情感分析',
  inputs=["世界充满了欺骗", "世界和平"],
  examples=[Example("基本都是欺骗", "消极"), Example("基本都是惊喜", "积极")],
  labels = ["消极", "积极"])
  
print('prediction: {}'.format(
       response.classifications))

Versioning

To use the SDK with a specific API version, you can specify it when creating the Modelfun Client:

import modelfun

mo = modelfun.Client('YOUR_API_KEY', '2022-08-08')

Endpoints

Modelfun Endpoint	Function
/generate	mo.generate()
/classify	mo.classify()

Models

When you call Modelfun's APIs we decide on a good default model for your use-case behind the scenes. The default model is great to get you started, but in production environments we recommend that you specify the model size yourself via the model parameter.

Responses

All of the endpoint functions will return a Modelfun object corresponding to the endpoint (e.g. for generation, it would be Generation). The responses can be found as instance variables of the object (e.g. generation would be Generation.text). Printing the Modelfun response object itself will display an organized view of the instance variables.