sentence-transformers How to use archit11/assesment_qwen3_embedding_06b_e3 with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("archit11/assesment_qwen3_embedding_06b_e3")
sentences = [
"parse sift error response body hyperswitch",
"// PATH: data/code_corpus_hyperswitch/crates__router__src__configs__settings.rs\n// MODULE: data::code_corpus_hyperswitch::crates__router__src__configs__settings.rs\n// SYMBOL: CmdLineConf\npub struct CmdLineConf {\n /// Config file.\n /// Application will look for \"config/config.toml\" if this option isn't specified.\n #[arg(short = 'f', long, value_name = \"FILE\")]\n pub config_path: Option<PathBuf>,\n}\n\n#[derive(Debug, Deserialize, Clone, Default)]\n#[serde(default)]",
"// PATH: data/code_corpus_hyperswitch/crates__analytics__src__query.rs\n// MODULE: data::code_corpus_hyperswitch::crates__analytics__src__query.rs\n// SYMBOL: Window\npub enum Window<R> {\n Sum {\n field: R,\n partition_by: Option<String>,\n order_by: Option<(String, Order)>,\n alias: Option<&'static str>,\n },\n RowNumber {\n field: R,\n partition_by: Option<String>,\n order_by: Option<(String, Order)>,\n alias: Option<&'static str>,\n },\n}\n\n#[derive(Debug, Clone, Copy)]",
"// PATH: data/code_corpus_hyperswitch/crates__hyperswitch_connectors__src__connectors__sift.rs\n// MODULE: data::code_corpus_hyperswitch::crates__hyperswitch_connectors__src__connectors__sift.rs\n// SYMBOL: build_error_response\n fn build_error_response(\n &self,\n res: Response,\n event_builder: Option<&mut ConnectorEvent>,\n ) -> CustomResult<ErrorResponse, errors::ConnectorError> {\n let response: sift::SiftErrorResponse = res\n .response\n .parse_struct(\"SiftErrorResponse\")\n .change_context(errors::ConnectorError::ResponseDeserializationFailed)?;\n\n event_builder.map(|i| i.set_response_body(&response));\n router_env::logger::info!(connector_response=?response);\n\n Ok(ErrorResponse {\n status_code: res.status_code,\n code: response.code,\n message: response.message,\n reason: response.reason,\n attempt_status: None,\n connector_transaction_id: None,\n connector_response_reference_id: None,\n network_advice_code: None,\n network_decline_code: None,\n network_error_message: None,\n connector_metadata: None,\n })\n }\n}"
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4] 
