---
library_name: transformers
tags:
- nlp
- classification
license: apache-2.0
datasets:
- zeyadusf/daigt
language:
- en
base_model:
- FacebookAI/roberta-base
pipeline_tag: text-classification
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->



## Model Details

 - eval_loss    : 0.02619364485144615,
 - eval_accuracy: 0.9941391941391942,
 - eval_f1-score: 0.9941391909936754,
 - epoch : 2.0



```
Classification Report:
              precision    recall  f1-score   support

           0       1.00      0.99      0.99      1365
           1       0.99      1.00      0.99      1365

    accuracy                           0.99      2730
   macro avg       0.99      0.99      0.99      2730
weighted avg       0.99      0.99      0.99      2730
````

![image/png](https://cdn-uploads.huggingface.co/production/uploads/65cf6fed0954f06e47d97e56/iTsf9imsdWtxtRaiMa5On.png)


#### Clean Function 
 - I used it when I tested manual the model and it gave good results when cleaning.
 ```python
import re
import html
def clean_text(text):
    # Remove HTML tags
    clean = re.compile('<.*?>')
    text = re.sub(clean, '', text)
    # Replace HTML entities with their corresponding characters
    text = html.unescape(text)
    # Remove extra whitespace and normalize spaces
    text = re.sub(r'\s+', ' ', text).strip()
    text = re.sub(r'[^a-zA-Z0-9\s]', '', text)
    return re.sub("\s\s+", " ", text)
```