Instructions to use dwisaji/SentimentBert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dwisaji/SentimentBert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dwisaji/SentimentBert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dwisaji/SentimentBert") model = AutoModelForSequenceClassification.from_pretrained("dwisaji/SentimentBert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 038bb2033dbc17043e03e3d67e156cef302b47cefe2044de5cda40e4e350e81f
- Size of remote file:
- 3.38 kB
- SHA256:
- 2036f3c869a26e3027ae81595bfe69fb249f55540557ef00078e10e819190a82
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