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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Bangkah/atha-text-classifier") model = AutoModelForSequenceClassification.from_pretrained("Bangkah/atha-text-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 225 Bytes
b1105fa | 1 2 3 4 5 6 7 8 9 10 | # 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 |