Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use HCKLab/BiBert-linear with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HCKLab/BiBert-linear with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HCKLab/BiBert-linear")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HCKLab/BiBert-linear") model = AutoModelForSequenceClassification.from_pretrained("HCKLab/BiBert-linear") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2857e0acfcf319e25431de4d938e72c3b33ca2af615211103e2a2fb0cccc2303
- Size of remote file:
- 669 MB
- SHA256:
- c34ecd72c79e297b8dfef330c29b74d82c8f0b64b60b554fda18e4948534cedb
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