Instructions to use slamos/bc-models-deberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use slamos/bc-models-deberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="slamos/bc-models-deberta")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("slamos/bc-models-deberta") model = AutoModelForSequenceClassification.from_pretrained("slamos/bc-models-deberta") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1795d6b6d3ee27488ab8bf3cbb322cb56eec72965f30fd8c40dd3255e16ae2fb
- Size of remote file:
- 740 MB
- SHA256:
- 21df6d6650b45bf325b8d8f0d58d778ae92f9b0a2f37c1310dba27fc578ee89c
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