Instructions to use andreyunic23/beds_step2_constraint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use andreyunic23/beds_step2_constraint with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="andreyunic23/beds_step2_constraint")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("andreyunic23/beds_step2_constraint") model = AutoModelForSequenceClassification.from_pretrained("andreyunic23/beds_step2_constraint") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5e0f420ea7d20f3278207241030579b4594fc12e2a6319b70d8b16208e98732d
- Size of remote file:
- 438 MB
- SHA256:
- d12fbd7cef63079e67b8f0b13846d2fdd66d63d582bd7b0113a15459c420b0c2
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