Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
roberta
feature-extraction
text-embeddings-inference
Instructions to use mchochlov/codebert-base-cd-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mchochlov/codebert-base-cd-ft with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mchochlov/codebert-base-cd-ft") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use mchochlov/codebert-base-cd-ft with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("mchochlov/codebert-base-cd-ft") model = AutoModel.from_pretrained("mchochlov/codebert-base-cd-ft") - Notebooks
- Google Colab
- Kaggle
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## Citing & Authors
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Please cite this paper if using the model.
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(To be published)
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Using a Nearest-Neighbour, BERT-Based Approach for Scalable Clone Detection Muslim Chochlov, Gul Aftab Ahmed, James Vincent Patten, Guoxian Lu, Wei Hou, David Gregg and Jim Buckley. International Conference on Software Maintenance and Engineering, 2022.
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