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:
- 6c4c36cf14c3213d7e0cd699d1e428af07b558dd424055c40b2c7728d6a19e4f
- Size of remote file:
- 3.44 kB
- SHA256:
- e9b6ca1f67217275a9519ac68d192f095674e9dbd9cd5c2a19856983c0da159d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.