modelfun 0.0.1.1

A Python library for the Modelfun API

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')

# generate a prediction for a prompt 
prediction = mo.generate(
            model_name='modelfun',
            prompt='新闻分类：\n今天（3日）稍早，中时新闻网、联合新闻网等台媒消息称，佩洛西3日上午抵台“立法院”，台湾新党一早8时就到台“立法院”外抗议，高喊：“佩洛西，滚蛋！”台媒报道称，新党主席吴成典表示，佩洛西来台一点道理都没有，“平常都说来者是客，但这次来的是祸！是来祸害台湾的。”他说，佩洛西给台湾带来祸害，“到底还要欢迎什么”。\n选项：财经，法律，国际，军事\n答案：')
            
# 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='意图分类',
  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", "中立"])
  
print('结果: {}'.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.