Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use HCKLab/BiBert-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HCKLab/BiBert-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HCKLab/BiBert-Classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HCKLab/BiBert-Classification") model = AutoModelForSequenceClassification.from_pretrained("HCKLab/BiBert-Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from HCKLab/BiBert-Classification: direct link, hf CLI and curl.
- Browser
- Download file 670 MB
-
https://huggingface.co/HCKLab/BiBert-Classification/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://HCKLab/BiBert-Classification@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/HCKLab/BiBert-Classification/resolve/refs%2Fpr%2F1/pytorch_model.bin
670 MB
- Xet hash:
- 2f1ae1315bcc4b4d0bb679c8de59cf560d9bb4e6aac0febb9ff4a18b3c110c96
- Size of remote file:
- 670 MB
- SHA256:
- 1fbaa50ab5a246bda17fdfbeda7ccc3395c55141babe2d83aca5f08236b74f88
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