Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use HCKLab/BiBert-Classification-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HCKLab/BiBert-Classification-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HCKLab/BiBert-Classification-1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HCKLab/BiBert-Classification-1") model = AutoModelForSequenceClassification.from_pretrained("HCKLab/BiBert-Classification-1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3648e9b58c864f1eeb6fa638db5799a9b2e776ff2b0a4b605e0eefcbb192b81e
- Size of remote file:
- 670 MB
- SHA256:
- eca4768ee408a71175056602959f8b718b47c72d92a882bdbdd0c9d774e056dc
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