Instructions to use hts98/hos_sentiment_bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hts98/hos_sentiment_bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hts98/hos_sentiment_bert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hts98/hos_sentiment_bert") model = AutoModelForSequenceClassification.from_pretrained("hts98/hos_sentiment_bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 378b98187de4b7939387f74d3542f7530d44d2ca4b42028d38359e33393e8da4
- Size of remote file:
- 5.37 kB
- SHA256:
- 7521dc96d7f72a11202f537d24664f90f619351a5b523981c7ad68a13a5001da
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