Text Classification
Transformers
Safetensors
English
roberta
code
classification
BERT
Python
Java
JavaScript
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("LavishKK/codebert-slowcode-detector")
model = AutoModelForSequenceClassification.from_pretrained("LavishKK/codebert-slowcode-detector")Quick Links
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Model Details
Model Description
- Developed by: Lavish Kamal Kumar
- License: Apache 2.0
- Finetuned from model: microsoft/codebert-base
Uses
This model is a code classifier designed to detect whether a given code snippet is fast or slow in terms of performance.
It is particularly useful for:
- Flagging potentially inefficient or unoptimized code
- Assisting automated code review tools
The model predicts one of two labels:
LABEL_0: Fast code (no major performance concerns)LABEL_1: Slow code (potential performance issues detected)
It works best on short to medium-length code snippets in supported programming languages and is intended for use with the 🤗 Transformers library.
Supported Languages
- Python
- Java
- JavaScript
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Model tree for LavishKK/codebert-slowcode-detector
Base model
microsoft/codebert-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LavishKK/codebert-slowcode-detector")