semvec/pypi-packages huggingface dataset:
modelfun 0.0.1.4
A Python library for the Modelfun API
Requires Python: >=3.6


# Please go to [MODELFUN](https://www.modelfun.cn/) 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`:

```bash
pip install --upgrade modelfun
```

Install from source:

```bash
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](https://www.modelfun.cn/). This is a basic example of the creating the client and using the `generate` endpoint.

### Generate
```python
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
```python
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:

```python
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.