Text Classification
Transformers
PyTorch
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use junzai/demo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use junzai/demo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="junzai/demo")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("junzai/demo") model = AutoModelForSequenceClassification.from_pretrained("junzai/demo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from junzai/demo: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/junzai/demo/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://junzai/demo/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/junzai/demo/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 03cbedd3fce785af7cf8f048e2d570657ea83e801646df3711ef4de2b7771fa4
- Size of remote file:
- 438 MB
- SHA256:
- 01e22cdea08982147bfec5be163e7f885ca791fabd0b6970cb608227ec2ea104
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.