Codebert Base Cd Ft

🚀 mchochlov/codebert-base-cd-ft

This is a sentence-transformers model that maps code to a 768-dimensional dense vector space. It's fine-tuned for clone detection using contrastive learning on parts of BigCloneBench code.

🚀 Quick Start

This section will guide you through using the mchochlov/codebert-base-cd-ft model in different ways.

✨ Features

• Maps code to a 768-dimensional dense vector space.
• Specifically fine-tuned for clone detection using contrastive learning on parts of BigCloneBench code.

📦 Installation

If you want to use this model, you need to install sentence-transformers first:

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage

If you have sentence-transformers installed, you can use the model like this:

from sentence_transformers import SentenceTransformer
code_fragments = [...]

model = SentenceTransformer('mchochlov/codebert-base-cd-ft')
embeddings = model.encode(code_fragments)
print(embeddings)

Advanced Usage

Without sentence-transformers, you can use the model as follows: First, pass your input through the transformer model, then apply the right pooling-operation on top of the contextualized word embeddings.

from transformers import AutoTokenizer, AutoModel
import torch


#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)


# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('mchochlov/codebert-base-cd-ft')
model = AutoModel.from_pretrained('mchochlov/codebert-base-cd-ft')

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)

# Perform pooling. In this case, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

print("Sentence embeddings:")
print(sentence_embeddings)

📚 Documentation

Evaluation Results

For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: RobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)

📄 License

No license information is provided in the original README.

📖 Citing & Authors

Please cite this paper if using the model.

@inproceedings{chochlov2022using,
  title={Using a Nearest-Neighbour, BERT-Based Approach for Scalable Clone Detection},
  author={Chochlov, Muslim and Ahmed, Gul Aftab and Patten, James Vincent and Lu, Guoxian and Hou, Wei and Gregg, David and Buckley, Jim},
  booktitle={2022 IEEE International Conference on Software Maintenance and Evolution (ICSME)},
  pages={582--591},
  year={2022},
  organization={IEEE}
}