BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 29
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Tolerblanc/klue-bert-finetuned")
model = AutoModelForSequenceClassification.from_pretrained("Tolerblanc/klue-bert-finetuned")Quick Links
K-urse_Detection_with_BERT
Overview
K-urse_Detection_with_BERT : Korean Cursing expression Detection with fine-tuned klue_BERT
This is the KWU "text mining" output for the first semester of 2023.
See Project Overview Here! : Notion(Korean)
See this model on GitHub : Link
Evaluation
- Comparison Model is here
- Used 2runo's Curse-detection-data
| Model/Metric | Accuracy | Precision | Recall | F1 Score |
|---|---|---|---|---|
| Comparison(Electra base) | 0.81 | 0.69 | 0.87 | 0.77 |
| klue-BERT base(Our best result) | 0.83 | 0.76** | 0.75 | 0.75 |
- Used Youtube Comments
| Model/Metric | Accuracy | Precision | Recall | F1 Score |
|---|---|---|---|---|
| Comparison(Electra base) | 0.77 | 0.52 | 0.90 | 0.66 |
| klue-BERT base(Our best result) | 0.89 | 0.75 | 0.80 | 0.78 |
Demo with HuggingFace's Space 🤗
Try Demo Here! Go to HuggingFace Space
Reference
- Smilegate-AI's Korean Unsmile Dataset : Link
- JeanLee's K-MHaS Dataset and Paper : Link
- KLUE(Korean Language Understanding Evaluation) BERT : Link
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding : Link
- 2runo's Curse Detection Dataset : Link
- JminJ's Bad Text Classifier : Link
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Tolerblanc/klue-bert-finetuned")