---
tags: autotrain
language: en
widget:
- text: "I quite enjoy using AutoTrain due to its simplicity."
datasets:
- hidude562/autotrain-data-SimpleDetect
co2_eq_emissions: 0.21691606119445225
---
# Model Description
This model detects if you are writing in a format that is more similar to Simple English Wikipedia or English Wikipedia. This can be extended to applications that aren't Wikipedia as well and to some extent, it can be used for other languages.

Please also note there is a major bias to special characters (Mainly the hyphen mark, but it also applies to others) so I would recommend removing them from your input text.

# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 837726721
- CO2 Emissions (in grams): 0.21691606119445225

## Validation Metrics

- Loss: 0.010096958838403225
- Accuracy: 0.996223414828066
- Macro F1: 0.996179398826373
- Micro F1: 0.996223414828066
- Weighted F1: 0.996223414828066
- Macro Precision: 0.996179398826373
- Micro Precision: 0.996223414828066
- Weighted Precision: 0.996223414828066
- Macro Recall: 0.996179398826373
- Micro Recall: 0.996223414828066
- Weighted Recall: 0.996223414828066


## Usage

You can use cURL to access this model:

```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I quite enjoy using AutoTrain due to its simplicity."}' https://api-inference.huggingface.co/models/hidude562/Wiki-Complexity
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("hidude562/Wiki-Complexity", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("hidude562/Wiki-Complexity", use_auth_token=True)

inputs = tokenizer("I quite enjoy using AutoTrain due to its simplicity.", return_tensors="pt")

outputs = model(**inputs)
```