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")# 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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b367b9057b2506a62aa378c407e3c6ff43f7e6a1e05456988feede08db2e75f1
- Size of remote file:
- 499 MB
- SHA256:
- 5148ce3882e5217a3e96997c6b8a917aecf37a09bc6df4a9a6a9f324727199a3
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