Instructions to use gokulsrinivasagan/codebert_base_code_uml_c with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gokulsrinivasagan/codebert_base_code_uml_c with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="gokulsrinivasagan/codebert_base_code_uml_c")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/codebert_base_code_uml_c") model = AutoModelForMaskedLM.from_pretrained("gokulsrinivasagan/codebert_base_code_uml_c", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from gokulsrinivasagan/codebert_base_code_uml_c: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/gokulsrinivasagan/codebert_base_code_uml_c/resolve/main/training_args.bin
- Command line
-
hf download hf://gokulsrinivasagan/codebert_base_code_uml_c/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/gokulsrinivasagan/codebert_base_code_uml_c/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- e165575ec7e6fdf7c91db1d8e51054fc3378435b3f3c633a47714cca2b9dcfb5
- Size of remote file:
- 5.37 kB
- SHA256:
- 5a1c1fe3765815e6573e881b085faa31e80eb0242d85746ba7548bb52f83298b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.