Instructions to use devnote5676/schwartz-values-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devnote5676/schwartz-values-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="devnote5676/schwartz-values-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("devnote5676/schwartz-values-classifier") model = AutoModelForSequenceClassification.from_pretrained("devnote5676/schwartz-values-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from devnote5676/schwartz-values-classifier: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/devnote5676/schwartz-values-classifier/resolve/main/model.safetensors
- Command line
-
hf download hf://devnote5676/schwartz-values-classifier/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/devnote5676/schwartz-values-classifier/resolve/main/model.safetensors
438 MB
- Xet hash:
- 63cfbd38f73327b480846dd9708c74cfc8f55ef099dcd722ba3bd62af93e7890
- Size of remote file:
- 438 MB
- SHA256:
- 14b5ca606f8ae85caf67df7ebfbc84cc03e0cbd5c39dbda0af47deff3b7c5277
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.