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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HCKLab/BiBert-Classification-1") model = AutoModelForSequenceClassification.from_pretrained("HCKLab/BiBert-Classification-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from HCKLab/BiBert-Classification-1: direct link, hf CLI and curl.
- Browser
- Download file 3.44 kB
-
https://huggingface.co/HCKLab/BiBert-Classification-1/resolve/main/training_args.bin
- Command line
-
hf download hf://HCKLab/BiBert-Classification-1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/HCKLab/BiBert-Classification-1/resolve/main/training_args.bin
3.44 kB
- Xet hash:
- 6c4c36cf14c3213d7e0cd699d1e428af07b558dd424055c40b2c7728d6a19e4f
- Size of remote file:
- 3.44 kB
- SHA256:
- e9b6ca1f67217275a9519ac68d192f095674e9dbd9cd5c2a19856983c0da159d
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