Shuu12121 / CodeSearch-ModernBERT-Owl-Plus

SentenceTransformer based on Shuu12121/CodeModernBERT-Owl-1.0

This is a sentence-transformers model finetuned from Shuu12121/CodeModernBERT-Owl-1.0 . It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: Shuu12121/CodeModernBERT-Owl-1.0
• Maximum Sequence Length: 1024 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: ModernBertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    "// SetGameSessionQueueArns sets the GameSessionQueueArns field's value.",
    'func (s *MatchmakingConfiguration) SetGameSessionQueueArns(v []*string) *MatchmakingConfiguration {\n\ts.GameSessionQueueArns = v\n\treturn s\n}',
    'protected function _beforeDispatch(DispatcherContext $context)\n    {\n        // Check if the user has been explicitly authenticated for this request\n        if (!$this->getUser()->isAuthentic(true))\n        {\n            foreach($this->__authenticator_queue as $authenticator)\n            {\n                if($authenticator->authenticateRequest($context) === true) {\n                    break;\n                }\n            }\n        }\n\n    }',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Training Details

Training Dataset

Unnamed Dataset

• Size: 1,736,166 training samples
• Columns: sentence_0 , sentence_1 , and label
• Approximate statistics based on the first 1000 samples:

  	sentence_0	sentence_1	label
  type	string	string	float
  details	min: 3 tokens mean: 48.85 tokens max: 1024 tokens	min: 30 tokens mean: 171.5 tokens max: 1024 tokens	min: 1.0 mean: 1.0 max: 1.0

• Samples:

  sentence_0	sentence_1	label
  // convDataValidationOperatior get excel data validation operator.	func convDataValidationOperatior(o DataValidationOperator) string { typeMap := map[DataValidationOperator]string{ DataValidationOperatorBetween: "between", DataValidationOperatorEqual: "equal", DataValidationOperatorGreaterThan: "greaterThan", DataValidationOperatorGreaterThanOrEqual: "greaterThanOrEqual", DataValidationOperatorLessThan: "lessThan", DataValidationOperatorLessThanOrEqual: "lessThanOrEqual", DataValidationOperatorNotBetween: "notBetween", DataValidationOperatorNotEqual: "notEqual", } return typeMap[o] }	1.0
  // Convert_v1_PodSecurityPolicyReview_To_security_PodSecurityPolicyReview is an autogenerated conversion function.	func Convert_v1_PodSecurityPolicyReview_To_security_PodSecurityPolicyReview(in *v1.PodSecurityPolicyReview, out *security.PodSecurityPolicyReview, s conversion.Scope) error { return autoConvert_v1_PodSecurityPolicyReview_To_security_PodSecurityPolicyReview(in, out, s) }	1.0
  // Of note, removeSegments() keeps the ordering of the results stable.	func removeSegments(segments []Segment, toRemove []Segment) []Segment { rv := make([]Segment, 0, len(segments)-len(toRemove)) OUTER: for _, segment := range segments { for _, r := range toRemove { if segment == r { continue OUTER } } rv = append(rv, segment) } return rv }	1.0

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size : 256
• per_device_eval_batch_size : 256
• num_train_epochs : 5
• fp16 : True
• multi_dataset_batch_sampler : round_robin

All Hyperparameters

Training Logs

Framework Versions

• Python: 3.11.12
• Sentence Transformers: 3.4.1
• Transformers: 4.51.3
• PyTorch: 2.6.0+cu124
• Accelerate: 1.6.0
• Datasets: 3.5.1
• Tokenizers: 0.21.1

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}