semvec/pypi-packages huggingface dataset:
img-en 0.1
It takes img and int zoom level and output zoomed image.
License: MIT
Keywords: zoom

# image-enlarge
It takes img and int zoom level and output zoomed image.

##Installation
```pip install img_en```

## How to use it?

## Example
  ```
   import img_en
   #importing module

   img = img_en.load("image.jpg")
   #loading image

   zoom = 2 
   #declaring int value for level of zoom

   zoomimg = img_en.enlarge(img,zoom) 
   #zooming image

   img_en.save("zoomimage.jpg",zoomimg)
   #saving zoom image 

   img_en.show("title",zoomimg)
   #displaying image

   img_en.wait(0)
   #0 for stop
   #1 for don't stop
smaberta 0.0.2
a wrapper for the huggingface transformer libraries
License: MIT
Keywords: nlp transformers classification text-classification fine-tuning

# SMaBERTa
This repository contains the code for SMaBERTa, a wrapper for the huggingface transformer libraries.
It was developed by Zhanna Terechshenko and Vishakh Padmakumar through research at the Center for 
Social Media and Politics at NYU.

## Setup

To install using pip, run
```
pip install smaberta
```

To install from the source, first download the repository by running 

```
git clone https://github.com/SMAPPNYU/SMaBERTa.git
```

Then, install the dependencies for this repo and setup by running
```
cd SMaBERTa
pip install -r requirements.txt
python setup.py install
```

## Using the package

Basic use:

```
from smaberta import TransformerModel

epochs = 3
lr = 4e-6

training_sample = ['Today is a great day', 'Today is a terrible day']
training_labels = [1, 0]

model = TransformerModel('roberta', 'roberta-base', num_labels=25, 'reprocess_input_data': True, "num_train_epochs":epochs, "learning_rate":lr,    
                         'output_dir':'./saved_model/', 'overwrite_output_dir': True, 'fp16':False)

model.train_model(training_sample, training_labels)

```

For further details, see `Tutorial.ipynb` in the (examples)[https://github.com/SMAPPNYU/SMaBERTa/tree/master/examples] directory.

# Acknowledgements 

Code for this project was adapted from version 0.6 of https://github.com/ThilinaRajapakse/simpletransformers

Vishakh Padmakumar and Zhanna Terechshenko contributed to the software writing, implementation, and testing.

Megan Brown contributed to documentation and publication.