Instructions to use Jeevesh8/std_pnt_04_feather_berts-49 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/std_pnt_04_feather_berts-49 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/std_pnt_04_feather_berts-49")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/std_pnt_04_feather_berts-49") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/std_pnt_04_feather_berts-49") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6d3318e7558d77ca847b6776d8136ce2d70df1c09d5d53e012bcb141d6206fef
- Size of remote file:
- 438 MB
- SHA256:
- eef13dc7d3d70782f2b7215ec71655132de742277e466a72ecaf1bdc4a3e9160
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