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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload error_analysis.md with huggingface_hub
Browse files- error_analysis.md +10 -0
error_analysis.md
ADDED
|
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# Error Analysis
|
| 2 |
+
|
| 3 |
+
- Total validation samples: 300
|
| 4 |
+
- Misclassified samples: 0
|
| 5 |
+
- Error rate: 0.0000
|
| 6 |
+
|
| 7 |
+
## Top Misclassifications (highest confidence)
|
| 8 |
+
- Tidak ada error pada validation set.
|
| 9 |
+
|
| 10 |
+
CSV detail: error_analysis.csv
|