Instructions to use Cbelem/scibert-certainty-classif with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cbelem/scibert-certainty-classif with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Cbelem/scibert-certainty-classif")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Cbelem/scibert-certainty-classif") model = AutoModelForSequenceClassification.from_pretrained("Cbelem/scibert-certainty-classif") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 296fcd4c600b3df0ef134f483270096b47803aa0e0c28d2672a8ddb4f2e7671a
- Size of remote file:
- 440 MB
- SHA256:
- 4597ed00e87d95670d7f79c5b8a61e34d34297a9a4c638b1fef58bd5c05d3482
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