Instructions to use techthiyanes/Bert_Bahasa_Sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use techthiyanes/Bert_Bahasa_Sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="techthiyanes/Bert_Bahasa_Sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("techthiyanes/Bert_Bahasa_Sentiment") model = AutoModelForSequenceClassification.from_pretrained("techthiyanes/Bert_Bahasa_Sentiment") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f7e1c2a167e02dc6e9e28a12463c40fce5983390f162e6f3451d114a562a4a30
- Size of remote file:
- 669 MB
- SHA256:
- 2965e350a8687281c36741f60058a9656719fd7f0eaa2cdce865aec67b325376
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.