Instructions to use Mallard74/codebert-base-ft-xcodeval with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mallard74/codebert-base-ft-xcodeval with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Mallard74/codebert-base-ft-xcodeval")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Mallard74/codebert-base-ft-xcodeval") model = AutoModelForSequenceClassification.from_pretrained("Mallard74/codebert-base-ft-xcodeval", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from Mallard74/codebert-base-ft-xcodeval: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/Mallard74/codebert-base-ft-xcodeval/resolve/main/model.safetensors
- Command line
-
hf download hf://Mallard74/codebert-base-ft-xcodeval/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/Mallard74/codebert-base-ft-xcodeval/resolve/main/model.safetensors
499 MB
- Xet hash:
- bf9e20afb8d6f18566d9ff58c54849386545be2e0451f9ad1334778fcc6688ca
- Size of remote file:
- 499 MB
- SHA256:
- bd913760e83a7cee01ff24be245df7c69df8ab6b57d598d87161a4d30d921490
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.