Instructions to use wiorz/bert_sm_gen1_defined_cv_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wiorz/bert_sm_gen1_defined_cv_3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="wiorz/bert_sm_gen1_defined_cv_3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("wiorz/bert_sm_gen1_defined_cv_3") model = AutoModelForSequenceClassification.from_pretrained("wiorz/bert_sm_gen1_defined_cv_3") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 707ea6be60f671b855d5f0ec224c0e14a4ffbb933d9b162c93bb4e9eef80aecf
- Size of remote file:
- 438 MB
- SHA256:
- 9716510c5b33c3c5f14da6136301a546bb10c5bf688f60bca0ec60a523bf19ac
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