Instructions to use Delicia/BiKirKinModelClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Delicia/BiKirKinModelClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Delicia/BiKirKinModelClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Delicia/BiKirKinModelClassification") model = AutoModelForSequenceClassification.from_pretrained("Delicia/BiKirKinModelClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from Delicia/BiKirKinModelClassification: direct link, hf CLI and curl.
- Browser
- Download file 2.24 GB
-
https://huggingface.co/Delicia/BiKirKinModelClassification/resolve/main/model.safetensors
- Command line
-
hf download hf://Delicia/BiKirKinModelClassification/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/Delicia/BiKirKinModelClassification/resolve/main/model.safetensors
2.24 GB
- Xet hash:
- 5122d1e987bfc7d23cea73d8e11768192c61a84f5a9b9a9b58458004d2dc5df2
- Size of remote file:
- 2.24 GB
- SHA256:
- 4d2fd4fdf6a027ee06c3da5bccd13b3754bb831a83ae863e142a4e4d6b3f35f7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.