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 training_args.bin from HCKLab/BiBert-Classification: direct link, hf CLI and curl.
- Browser
- Download file 3.38 kB
-
https://huggingface.co/HCKLab/BiBert-Classification/resolve/main/training_args.bin
- Command line
-
hf download hf://HCKLab/BiBert-Classification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/HCKLab/BiBert-Classification/resolve/main/training_args.bin
3.38 kB
- Xet hash:
- e71ed4ddf686b1a52472118d2cd14fc0a49583109e35bc948a1d8974149088ea
- Size of remote file:
- 3.38 kB
- SHA256:
- fdcceb88133e8ecd89ecf48ba39bf671b350b08d3ebca790be64d33b43c2bc88
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