Text Classification
Transformers
Safetensors
PyTorch
Indonesian
bert
sentiment-analysis
socmed-classification
text-embeddings-inference
Instructions to use djnsfull/sentiment-socmed-1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use djnsfull/sentiment-socmed-1.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="djnsfull/sentiment-socmed-1.0")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("djnsfull/sentiment-socmed-1.0") model = AutoModelForSequenceClassification.from_pretrained("djnsfull/sentiment-socmed-1.0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Model tree for djnsfull/sentiment-socmed-1.0
Base model
indobenchmark/indobert-large-p2