Text Classification
Transformers
PyTorch
JAX
Safetensors
code
English
roberta
text-embeddings-inference
Instructions to use Fsoft-AIC/Codebert-docstring-inconsistency with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Fsoft-AIC/Codebert-docstring-inconsistency with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Fsoft-AIC/Codebert-docstring-inconsistency")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Fsoft-AIC/Codebert-docstring-inconsistency") model = AutoModelForSequenceClassification.from_pretrained("Fsoft-AIC/Codebert-docstring-inconsistency") - Notebooks
- Google Colab
- Kaggle
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## Model Description
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This model is developed based on [Codebert](https://github.com/microsoft/CodeBERT) and a 5M subset of [The Vault](https://huggingface.co/datasets/Fsoft-AIC/thevault-function-level) to detect the inconsistency between docstring/comment and function. It is used to remove
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More information:
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- **Repository:** [FSoft-AI4Code/TheVault](https://github.com/FSoft-AI4Code/TheVault)
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## Model Description
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This model is developed based on [Codebert](https://github.com/microsoft/CodeBERT) and a 5M subset of [The Vault](https://huggingface.co/datasets/Fsoft-AIC/thevault-function-level) to detect the inconsistency between docstring/comment and function. It is used to remove noisy examples in The Vault dataset.
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More information:
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- **Repository:** [FSoft-AI4Code/TheVault](https://github.com/FSoft-AI4Code/TheVault)
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