Instructions to use neulab/codebert-python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use neulab/codebert-python with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="neulab/codebert-python")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("neulab/codebert-python") model = AutoModelForMaskedLM.from_pretrained("neulab/codebert-python", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download model.safetensors from neulab/codebert-python: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/neulab/codebert-python/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://neulab/codebert-python@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/neulab/codebert-python/resolve/refs%2Fpr%2F1/model.safetensors
499 MB
- Xet hash:
- 43048383f3153b69e0127dba9b7415a4b2c1ff449323be20038787527eae1d7c
- Size of remote file:
- 499 MB
- SHA256:
- 41880d61f0331579ae0dc7ac3bde105b240d04083ff1c8b239ddd1c1c67fc941
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.