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")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("teguholix/BERTAUT") model = AutoModelForSequenceClassification.from_pretrained("teguholix/BERTAUT") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e2753ee7c50e019cbce60fbf582b8f45344fbf953ac04c1ce2c004de77a2f6a4
- Size of remote file:
- 5.2 kB
- SHA256:
- a2138962ee4cbff7037966a2eb4667517b6844f2bbc543bfdcfea8abb12c2191
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