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:
- c2b1bb92480471c7edeef2ae7d7894ce85d89daf281ebe2aaabe1bdcf62e5e90
- Size of remote file:
- 443 MB
- SHA256:
- 24b4ad974cd94c255221b55abdf00fb09f52798f6e266961029ea48f8e89ea34
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