Instructions to use sbcBI/sentiment_analysis_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sbcBI/sentiment_analysis_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sbcBI/sentiment_analysis_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sbcBI/sentiment_analysis_model") model = AutoModelForSequenceClassification.from_pretrained("sbcBI/sentiment_analysis_model") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0c8a39630774d055637ada8dc7305374b6bfa6ce5ee6a0e0394a8a6989b207de
- Size of remote file:
- 268 MB
- SHA256:
- c846b0047c66c9175d6f8fbb4097b132084f3ddd1a44fb4a8595dcaeb0897acb
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