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roberta-DAIGT-kaggle
zeyadusf/roberta-DAIGT-kaggle
roberta-DAIGT-kaggle is a text classification model from zeyadusf. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
- evalloss : 0.02619364485144615, - evalaccuracy: 0.9941391941391942, - evalf1-score: 0.9941391909936754, - epoch : 2.0
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- roberta
- Access
- Public
- Created
- May 1, 2024
- Updated
- Sep 22, 2024
- SHA
- ef51d3f8
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Base models
Datasets
Model card
From the Hugging Face model README
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
Clean Function
- I used it when I tested manual the model and it gave good results when cleaning.
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)
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- License
- apache-2.0
- Languages
- en
- Created
- May 1, 2024
- Updated
- Sep 22, 2024