Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use HCKLab/BiBert-Classification-V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HCKLab/BiBert-Classification-V2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HCKLab/BiBert-Classification-V2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HCKLab/BiBert-Classification-V2") model = AutoModelForSequenceClassification.from_pretrained("HCKLab/BiBert-Classification-V2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from HCKLab/BiBert-Classification-V2: direct link, hf CLI and curl.
- Browser
- Download file 670 MB
-
https://huggingface.co/HCKLab/BiBert-Classification-V2/resolve/dca080caacb2cb05994cd3e4d15a494a3e9bf14c/pytorch_model.bin
- Command line
-
hf download hf://HCKLab/BiBert-Classification-V2@dca080caacb2cb05994cd3e4d15a494a3e9bf14c/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/HCKLab/BiBert-Classification-V2/resolve/dca080caacb2cb05994cd3e4d15a494a3e9bf14c/pytorch_model.bin
670 MB
- Xet hash:
- 10d734f83b81c7b3647c7169c40bffa9821df1c4647767a0d8fcbf7e71ec6968
- Size of remote file:
- 670 MB
- SHA256:
- 99266982619885cc65356ce6ccd93b7186a22f79e92660939053b029f044c742
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