Instructions to use ctoraman/deprem-mdeberta-binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ctoraman/deprem-mdeberta-binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ctoraman/deprem-mdeberta-binary")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ctoraman/deprem-mdeberta-binary") model = AutoModelForSequenceClassification.from_pretrained("ctoraman/deprem-mdeberta-binary") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2783c150917c86d940b44c96d3d4fd3fda3e7956df1cd0c66edf83d42c0baff8
- Size of remote file:
- 1.12 GB
- SHA256:
- 3f586c1b7bce064710c4f3533bf1fc293dcb0a5b51721ff04e1b6e317897407c
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