--- language: - zh base_model: OpenSearch-AI/Ops-MoA-Yuan-embedding-1.0 model-index: - name: Ops-MoA-Yuan-embedding-1.0 results: - task: type: Retrieval dataset: type: C-MTEB/CmedqaRetrieval name: MTEB CmedqaRetrieval config: default split: dev revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301 metrics: - type: ndcg_at_10 value: 51.461 - task: type: Retrieval dataset: type: C-MTEB/CovidRetrieval name: MTEB CovidRetrieval config: default split: dev revision: 1271c7809071a13532e05f25fb53511ffce77117 metrics: - type: ndcg_at_10 value: 93.2 - task: type: Retrieval dataset: type: C-MTEB/DuRetrieval name: MTEB DuRetrieval config: default split: dev revision: a1a333e290fe30b10f3f56498e3a0d911a693ced metrics: - type: ndcg_at_10 value: 89.84 - task: type: Retrieval dataset: type: C-MTEB/EcomRetrieval name: MTEB EcomRetrieval config: default split: dev revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9 metrics: - type: ndcg_at_10 value: 71.084 - task: type: Retrieval dataset: type: C-MTEB/MMarcoRetrieval name: MTEB MMarcoRetrieval config: default split: dev revision: 539bbde593d947e2a124ba72651aafc09eb33fc2 metrics: - type: ndcg_at_10 value: 82.43 - task: type: Retrieval dataset: type: C-MTEB/MedicalRetrieval name: MTEB MedicalRetrieval config: default split: dev revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6 metrics: - type: ndcg_at_10 value: 74.848 - task: type: Retrieval dataset: type: C-MTEB/T2Retrieval name: MTEB T2Retrieval config: default split: dev revision: 8731a845f1bf500a4f111cf1070785c793d10e64 metrics: - type: ndcg_at_10 value: 85.784 - task: type: Retrieval dataset: type: C-MTEB/VideoRetrieval name: MTEB VideoRetrieval config: default split: dev revision: 58c2597a5943a2ba48f4668c3b90d796283c5639 metrics: - type: ndcg_at_10 value: 79.513 pipeline_tag: feature-extraction tags: - mteb --- ```python import torch.nn as nn from sentence_transformers import SentenceTransformer from modeling_adaptor import MixtureOfAdaptors class CustomSentenceTransformer(nn.Module): def __init__(self, output_dim=1536): super(CustomSentenceTransformer, self).__init__() self.model = SentenceTransformer('IEITYuan/Yuan-embedding-1.0', trust_remote_code=True) adaptor = MixtureOfAdaptors(5, 1792) adaptor.load_state_dict(torch.load(f"yuan-adaptors.pth")) self.model.add_module('adaptor', adaptor) self.output_dim = output_dim def encode(self, sentences, **kwargs): embeddings = self.model.encode(sentences, **kwargs) return embeddings[:, :self.output_dim] model = CustomSentenceTransformer(output_dim=1536) model.encode(['text'])