Instructions to use codeaze/deberta_large_23feb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use codeaze/deberta_large_23feb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="codeaze/deberta_large_23feb")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("codeaze/deberta_large_23feb") model = AutoModelForTokenClassification.from_pretrained("codeaze/deberta_large_23feb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download spm.model from codeaze/deberta_large_23feb: direct link, hf CLI and curl.
- Browser
- Download file 2.46 MB
-
https://huggingface.co/codeaze/deberta_large_23feb/resolve/main/spm.model
- Command line
-
hf download hf://codeaze/deberta_large_23feb/spm.model
-
curl -L -o spm.model https://huggingface.co/codeaze/deberta_large_23feb/resolve/main/spm.model
2.46 MB
- Xet hash:
- ceef70ee5c068f2e8ec2ea787d1566d2f5846b612f0e0a3879edffa246dc91cf
- Size of remote file:
- 2.46 MB
- SHA256:
- c679fbf93643d19aab7ee10c0b99e460bdbc02fedf34b92b05af343b4af586fd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.