metadata

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)