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:
- 005e8c2e819012b63dbdf2c354f350b9959e9457195b7957b9a8d22070df9b21
- Size of remote file:
- 3.38 kB
- SHA256:
- 9e5ef96f81a4e018e78b9fcd709ac276a8da63d6e2028183fb262219f2c45d34
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