microsoft/codebert-base-mlm
A pre-trained language model designed to handle both programming languages and natural languages. With a multi-task learning framework that includes masked language modeling, next sentence prediction, and replaced token detection, CodeBERT achieves state-of-the-art results on various code understanding tasks while also performing well on natural language processing benchmarks. We analyze the effects of different design choices and provide insights into the behavior of CodeBERT, demonstrating its potential as a versatile tool for a wide range of applications involving both coding and natural language understanding.
A pre-trained language model designed to handle both programming languages and natural languages. With a multi-task learning framework that includes masked language modeling, next sentence prediction, and replaced token detection, CodeBERT achieves state-of-the-art results on various code understanding tasks while also performing well on natural language processing benchmarks. We analyze the effects of different design choices and provide insights into the behavior of CodeBERT, demonstrating its potential as a versatile tool for a wide range of applications involving both coding and natural language understanding.
HTTP/cURL API
You can use cURL or any other http client to run inferences:
curl -X POST \
-d '{"input": "Where is my <mask>?"}' \
-H "Authorization: bearer $DEEPINFRA_TOKEN" \
-H 'Content-Type: application/json' \
'https://api.deepinfra.com/v1/inference/microsoft/codebert-base-mlm'
which will give you back something similar to:
{
"results": [
{
"sequence": "where is my father?",
"score": 0.08898820728063583,
"token": 2269,
"token_str": "father"
},
{
"sequence": "where is my mother?",
"score": 0.07864926755428314,
"token": 2388,
"token_str": "mother"
}
],
"request_id": null,
"inference_status": {
"status": "unknown",
"runtime_ms": 0,
"cost": 0.0,
"tokens_generated": 0,
"tokens_input": 0
}
}
Input fields
webhookfile
The webhook to call when inference is done, by default you will get the output in the response of your inference request
