Instructions to use Sheza/bert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sheza/bert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sheza/bert-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sheza/bert-base") model = AutoModelForSequenceClassification.from_pretrained("Sheza/bert-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4fa10d32838dd16d00ff96ddf184017d287f876b9b5b03aea2c8332a18e2b479
- Size of remote file:
- 433 MB
- SHA256:
- 60a0e6aa8ff2aa02047f16c4785e7d8acf161acd7c5258e62adb7e40b58e083e
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