Instructions to use andreyunic23/beds_step3_constraint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use andreyunic23/beds_step3_constraint with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="andreyunic23/beds_step3_constraint")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("andreyunic23/beds_step3_constraint") model = AutoModelForSequenceClassification.from_pretrained("andreyunic23/beds_step3_constraint") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 767d6ac400d31c7c71732d521aa5c4b546cbd03327bf82acd3bf31140410b731
- Size of remote file:
- 438 MB
- SHA256:
- 75ebf3f4aab9f900cb1e5f2469c863afc97451bb79577bc2db0c3a9be583bf7c
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