Text Classification
Transformers
Safetensors
English
roberta
code
classification
BERT
Python
Java
JavaScript
text-embeddings-inference
Instructions to use LavishKK/codebert-slowcode-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LavishKK/codebert-slowcode-detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LavishKK/codebert-slowcode-detector")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LavishKK/codebert-slowcode-detector") model = AutoModelForSequenceClassification.from_pretrained("LavishKK/codebert-slowcode-detector", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9ea8c146c874d9baad49bb608d7d3e5d711532259a2657abe8641c5cc16f6e47
- Size of remote file:
- 499 MB
- SHA256:
- d905719a5d8388fd0860110b57bb3fcc3e7e79bc87fdf7ce909f5fd4dbb86d8f
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