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 model.safetensors from HCKLab/BiBert-Classification: direct link, hf CLI and curl.
- Browser
- Download file 669 MB
-
https://huggingface.co/HCKLab/BiBert-Classification/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://HCKLab/BiBert-Classification@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/HCKLab/BiBert-Classification/resolve/refs%2Fpr%2F1/model.safetensors
669 MB
- Xet hash:
- 12230599ad17f120e4516704e7157608fa0f5d77dd0fb2aea9d7f05ea72c62dd
- Size of remote file:
- 669 MB
- SHA256:
- 9512126f18dab5d745013559589d3c8b1ad99549fbb0b5b62aeaee0e0bf38ac0
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