Instructions to use emaeon/trained_cppbert1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use emaeon/trained_cppbert1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="emaeon/trained_cppbert1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("emaeon/trained_cppbert1") model = AutoModelForSequenceClassification.from_pretrained("emaeon/trained_cppbert1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from emaeon/trained_cppbert1: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/emaeon/trained_cppbert1/resolve/main/model.safetensors
- Command line
-
hf download hf://emaeon/trained_cppbert1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/emaeon/trained_cppbert1/resolve/main/model.safetensors
499 MB
- Xet hash:
- 4e776cf838f0417d1348af8a7cd25e9681041d93eacf4769c6a8a554ef1ac83a
- Size of remote file:
- 499 MB
- SHA256:
- 291d328cfc79350db954f7ba4f4e7593a2c914808c307a99bfc58840aebc0d5f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.