Text Classification
Transformers
Safetensors
Indonesian
English
multilingual
bert
mbert
utaut
technology-acceptance
indonesian
spam-detection
user-review-analysis
text-embeddings-inference
Instructions to use teguholix/BERTAUT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use teguholix/BERTAUT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="teguholix/BERTAUT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("teguholix/BERTAUT") model = AutoModelForSequenceClassification.from_pretrained("teguholix/BERTAUT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 675 Bytes
e9d8f28 956fe12 | 1 2 3 4 5 6 7 8 9 10 11 | ,precision,recall,f1-score,support
PE,0.8996383363471971,0.9369114877589454,0.9178966789667896,1062.0
EE,0.9130841121495327,0.9208294062205467,0.9169404035664007,1061.0
SI,0.8810810810810811,0.9209039548022598,0.9005524861878453,1062.0
FC,0.8942652329749103,0.9397363465160076,0.9164370982552801,1062.0
GR,0.9596456692913385,0.9180790960451978,0.9384023099133783,1062.0
SP,0.9390756302521008,0.8426013195098964,0.8882265275707899,1061.0
accuracy,0.9131868131868132,0.9131868131868132,0.9131868131868132,0.9131868131868132
macro avg,0.9144650103493602,0.9131769351421423,0.9130759174100808,6370.0
weighted avg,0.9144613636112592,0.9131868131868132,0.913079211743469,6370.0
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