---
language: en
license: mit
---

# model-card-testing

## Model Details

- **Developed by:** testing author
- **Model type:** testing type
- **Language(s):** Language(s) of the model
- **License:** License for the model
- **Model Description:** testing description

## Intended Uses & Limitations

This model can be used for .

#### How to Use

You can get started with the model with code like the following:

```python
# You can include sample code which will be formatted
```

Here is how to use this model to get the features of a given text in Pytorch: 

```python
# PyTorch usage
```

and in TensorFlow: 

```python
# Tensorflow usage
```

#### Limitations and Bias

**CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propogate historical and current stereotypes.**

This model can have biased predictions. For example: 

```python
# Example of biased predictions
```

## Training Data

Describe the data you used to train the model.
If you initialized it with pre-trained weights, add a link to the pre-trained model card or repository with description of the pre-training data.

## Training Procedure

Preprocessing, hardware used, hyperparameters...

## Evaluation Results

Provide some evaluation results.

## Environmental Impact

You can estimate carbon emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700)

- **Hardware Type:** 
- **Hours used:** 
- **Cloud Provider:** 
- **Compute Region:** 
- **Carbon Emitted** *(Power consumption x Time x Carbon produced based on location of power grid)*: 

### BibTeX Entry and Citation Info

```bibtex
@inproceedings{...,
  year={2020}
}
```