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
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Download error_analysis.md from Bangkah/atha-text-classifier: direct link, hf CLI and curl.
- Browser
- Download file 225 Bytes
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https://huggingface.co/Bangkah/atha-text-classifier/resolve/main/error_analysis.md
- Command line
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hf download hf://Bangkah/atha-text-classifier/error_analysis.md
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curl -L -o error_analysis.md https://huggingface.co/Bangkah/atha-text-classifier/resolve/main/error_analysis.md
225 Bytes
| # 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 |