Text Classification
Transformers
Safetensors
English
Indonesian
bert
legal
text-embeddings-inference
Instructions to use Rendika/tweets-election-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rendika/tweets-election-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Rendika/tweets-election-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Rendika/tweets-election-classification") model = AutoModelForSequenceClassification.from_pretrained("Rendika/tweets-election-classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 08d8694be6637617402922bb25848d100552f8960b817d37cc02108596598d99
- Size of remote file:
- 442 MB
- SHA256:
- 680caef32c9cae7df62b0c4c53b311aec2b49dd6ec8d81c9421615e04bd640ae
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