Instructions to use mrm8488/codebert-finetuned-clone-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrm8488/codebert-finetuned-clone-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="mrm8488/codebert-finetuned-clone-detection")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("mrm8488/codebert-finetuned-clone-detection") model = AutoModel.from_pretrained("mrm8488/codebert-finetuned-clone-detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from mrm8488/codebert-finetuned-clone-detection: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/mrm8488/codebert-finetuned-clone-detection/resolve/refs%2Fpr%2F2/flax_model.msgpack
- Command line
-
hf download hf://mrm8488/codebert-finetuned-clone-detection@refs/pr/2/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/mrm8488/codebert-finetuned-clone-detection/resolve/refs%2Fpr%2F2/flax_model.msgpack
499 MB
- Xet hash:
- bc2f5bbbb6d77cbecf9435c23467b70e3d67a342f164c6e685183d9f772e00f5
- Size of remote file:
- 499 MB
- SHA256:
- 6e36187e1ab6f34c7ce201bd481f869e2e13cce96dceb330418aaeb2d47c8e74
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