microsoft-graphcodebert-base
Version: 10
GraphCodeBERT model
GraphCodeBERT is a graph-based pre-trained model based on the Transformer architecture for programming language, which also considers data-flow information along with code sequences. GraphCodeBERT consists of 12 layers, 768 dimensional hidden states, and 12 attention heads. The maximum sequence length for the model is 512. The model is trained on the CodeSearchNet dataset, which includes 2.3M functions with document pairs for six programming languages. More details can be found in the paper by Guo et. al. Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by the Hugging Face community members.microsoft/graphcodebert-base powered by Hugging Face Inference Toolkit
Send Request
You can use cURL or any REST Client to send a request to the AzureML endpoint with your AzureML token.curl <AZUREML_ENDPOINT_URL> \
-X POST \
-H "Authorization: Bearer <AZUREML_TOKEN>" \
-H "Content-Type: application/json" \
-d '{"inputs":"The answer to the universe is undefined."}'
Supported Parameters
- inputs (string): The text with masked tokens
- parameters (object):
- top_k (integer): When passed, overrides the number of predictions to return.
- targets (string[]): When passed, the model will limit the scores to the passed targets instead of looking up in the whole vocabulary. If the provided targets are not in the model vocab, they will be tokenized and the first resulting token will be used (with a warning, and that might be slower).
Model Specifications
LicenseUnknown
Last UpdatedJuly 2025
ProviderHuggingFace