---
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.

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)
```