Text Classification
Transformers
Safetensors
Indonesian
bert
sentiment-analysis
indonesian
text-embeddings-inference
Instructions to use Bangkah/atha-text-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bangkah/atha-text-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Bangkah/atha-text-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Bangkah/atha-text-classifier") model = AutoModelForSequenceClassification.from_pretrained("Bangkah/atha-text-classifier") - Notebooks
- Google Colab
- Kaggle
| # Error Analysis | |
| - Total validation samples: 300 | |
| - Misclassified samples: 0 | |
| - Error rate: 0.0000 | |
| ## Top Misclassifications (highest confidence) | |
| - Tidak ada error pada validation set. | |
| CSV detail: error_analysis.csv |