Instructions to use patrickxchong/bert-tiny-bahasa-cased-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use patrickxchong/bert-tiny-bahasa-cased-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="patrickxchong/bert-tiny-bahasa-cased-sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("patrickxchong/bert-tiny-bahasa-cased-sentiment") model = AutoModelForSequenceClassification.from_pretrained("patrickxchong/bert-tiny-bahasa-cased-sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 504b3dec8cbfee5b1a17ed268804d41dc573094734e51ae6aeacf0e7bcfcd560
- Size of remote file:
- 66 MB
- SHA256:
- 807c95d88ec7570eb5d9091a9f1eeea30795ef6f5fbea58c35cc1470a5192154
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.