SweRankEmbed-Large is a 7B bi-encoder for code retrieval. It significantly outperforms other embedding models on the issue localization task.

The model has been trained on large-scale issue localization data collected from public python github repositories. Check out our blog post and paper for more details!

You can combine SweRankEmbed with our or rerankers for even higher quality ranking performance.

Link to code: https://github.com/gangiswag/SweRank

Performance

SweRank models show SOTA localization performance on a variety of benchmarks like SWE-Bench-Lite and LocBench, considerably out-performing agent-based approaches relying on Claude-3.5

Requirements

transformers>=4.39.2
flash_attn>=2.5.6

Usage with Sentence-Transformers

from from sentence_transformers import SentenceTransformer

model = SentenceTransformer("Salesforce/SweRankEmbed-Large", trust_remote_code=True)
# In case you want to reduce the maximum length:
model.max_seq_length = 8192

queries = ['Calculate the n-th factorial']
documents = ['def fact(n):\n if n < 0:\n raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']

query_embeddings = model.encode(queries, prompt_name="query")
document_embeddings = model.encode(documents)

scores = query_embeddings @ document_embeddings.T

for query, query_scores in zip(queries, scores):
 doc_score_pairs = list(zip(documents, query_scores))
 doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
 # Output passages & scores
 print("Query:", query)
 for document, score in doc_score_pairs:
 print(score, document)

Observe the config_sentence_transformers.json to see all pre-built prompt names.

Usage with Huggingface Transformers

Important: the query prompt must include the following task instruction prefix: "*Instruct: Given a github issue, identify the code that needs to be changed to fix the issue.\nQuery: *"

import torch
import torch.nn.functional as F

from torch import Tensor
from transformers import AutoTokenizer, AutoModel

def last_token_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor:
 left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
 if left_padding:
 return last_hidden_states[:, -1]
 else:
 sequence_lengths = attention_mask.sum(dim=1) - 1
 batch_size = last_hidden_states.shape[0]
 return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]

def get_detailed_instruct(task_description: str, query: str) -> str:
 return f'Instruct: {task_description}\nQuery: {query}'

# Each query must come with a one-sentence instruction that describes the task
task = 'Given a github issue, identify the code that needs to be changed to fix the issue.'

tokenizer = AutoTokenizer.from_pretrained('Salesforce/SweRankEmbed-Large', trust_remote_code=True)
model = AutoModel.from_pretrained('Salesforce/SweRankEmbed-Large', trust_remote_code=True)
model.eval()

max_length = 8192

queries = ['Calculate the n-th factorial']
queries_with_prefix = [get_detailed_instruct(task, query) for query in queries]
query_inputs = tokenizer(queries_with_prefix, padding=True, truncation=True, return_tensors='pt', max_length=max_length)

documents = ['def fact(n):\n if n < 0:\n raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
document_inputs = tokenizer(documents, padding=True, truncation=True, return_tensors='pt', max_length=max_length)

# Compute token embeddings
with torch.no_grad():
 query_embeddings = last_token_pool(model(**query_inputs).last_hidden_state, query_inputs["attention_mask"]])
 document_embeddings = last_token_pool(model(**document_inputs).last_hidden_state, document_inputs["attention_mask"]])


# normalize embeddings
query_embeddings = torch.nn.functional.normalize(query_embeddings, p=2, dim=1)
document_embeddings = torch.nn.functional.normalize(document_embeddings, p=2, dim=1)

scores = torch.mm(query_embeddings, document_embeddings.transpose(0, 1))
for query, query_scores in zip(queries, scores):
 doc_score_pairs = list(zip(documents, query_scores))
 doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
 #Output passages & scores
 print("Query:", query)
 for document, score in doc_score_pairs:
 print(score, document)

Citation

If you find this model work useful in your research, please consider citing our paper:

@article{reddy2025swerank,
 title={SweRank: Software Issue Localization with Code Ranking},
 author={Reddy, Revanth Gangi and Suresh, Tarun and Doo, JaeHyeok and Liu, Ye and Nguyen, Xuan Phi and Zhou, Yingbo and Yavuz, Semih and Xiong, Caiming and Ji, Heng and Joty, Shafiq},
 journal={arXiv preprint arXiv:2505.07849},
 year={2025}
}

Base model