--- library_name: sentence-transformers model-index: - name: XYZ-embedding-zh results: - dataset: config: default name: MTEB CMedQAv1 revision: None split: test type: C-MTEB/CMedQAv1-reranking metrics: - type: map value: 89.61792115239176 - type: mrr value: 91.46722222222222 - type: main_score value: 89.61792115239176 task: type: Reranking - dataset: config: default name: MTEB CMedQAv2 revision: None split: test type: C-MTEB/CMedQAv2-reranking metrics: - type: map value: 89.22040591564271 - type: mrr value: 91.2995238095238 - type: main_score value: 89.22040591564271 task: type: Reranking - dataset: config: default name: MTEB CmedqaRetrieval revision: None split: dev type: C-MTEB/CmedqaRetrieval metrics: - type: map_at_1 value: 27.939000000000004 - type: map_at_10 value: 41.227999999999994 - type: map_at_100 value: 43.018 - type: map_at_1000 value: 43.120000000000005 - type: map_at_3 value: 36.895 - type: map_at_5 value: 39.373999999999995 - type: mrr_at_1 value: 42.136 - type: mrr_at_10 value: 50.394000000000005 - type: mrr_at_100 value: 51.288 - type: mrr_at_1000 value: 51.324000000000005 - type: mrr_at_3 value: 47.887 - type: mrr_at_5 value: 49.362 - type: ndcg_at_1 value: 42.136 - type: ndcg_at_10 value: 47.899 - type: ndcg_at_100 value: 54.730999999999995 - type: ndcg_at_1000 value: 56.462999999999994 - type: ndcg_at_3 value: 42.66 - type: ndcg_at_5 value: 44.913 - type: precision_at_1 value: 42.136 - type: precision_at_10 value: 10.52 - type: precision_at_100 value: 1.6070000000000002 - type: precision_at_1000 value: 0.183 - type: precision_at_3 value: 24.064 - type: precision_at_5 value: 17.374000000000002 - type: recall_at_1 value: 27.939000000000004 - type: recall_at_10 value: 58.29600000000001 - type: recall_at_100 value: 86.504 - type: recall_at_1000 value: 98.105 - type: recall_at_3 value: 42.475 - type: recall_at_5 value: 49.454 - type: main_score value: 47.899 task: type: Retrieval - dataset: config: default name: MTEB CovidRetrieval revision: None split: dev type: C-MTEB/CovidRetrieval metrics: - type: map_at_1 value: 77.371 - type: map_at_10 value: 85.229 - type: map_at_100 value: 85.358 - type: map_at_1000 value: 85.36 - type: map_at_3 value: 84.176 - type: map_at_5 value: 84.79299999999999 - type: mrr_at_1 value: 77.661 - type: mrr_at_10 value: 85.207 - type: mrr_at_100 value: 85.33699999999999 - type: mrr_at_1000 value: 85.339 - type: mrr_at_3 value: 84.229 - type: mrr_at_5 value: 84.79299999999999 - type: ndcg_at_1 value: 77.766 - type: ndcg_at_10 value: 88.237 - type: ndcg_at_100 value: 88.777 - type: ndcg_at_1000 value: 88.818 - type: ndcg_at_3 value: 86.16 - type: ndcg_at_5 value: 87.22 - type: precision_at_1 value: 77.766 - type: precision_at_10 value: 9.841999999999999 - type: precision_at_100 value: 1.0070000000000001 - type: precision_at_1000 value: 0.101 - type: precision_at_3 value: 30.875000000000004 - type: precision_at_5 value: 19.073 - type: recall_at_1 value: 77.371 - type: recall_at_10 value: 97.366 - type: recall_at_100 value: 99.684 - type: recall_at_1000 value: 100.0 - type: recall_at_3 value: 91.702 - type: recall_at_5 value: 94.31 - type: main_score value: 88.237 task: type: Retrieval - dataset: config: default name: MTEB DuRetrieval revision: None split: dev type: C-MTEB/DuRetrieval metrics: - type: map_at_1 value: 27.772000000000002 - type: map_at_10 value: 84.734 - type: map_at_100 value: 87.298 - type: map_at_1000 value: 87.32900000000001 - type: map_at_3 value: 59.431 - type: map_at_5 value: 74.82900000000001 - type: mrr_at_1 value: 93.65 - type: mrr_at_10 value: 95.568 - type: mrr_at_100 value: 95.608 - type: mrr_at_1000 value: 95.609 - type: mrr_at_3 value: 95.267 - type: mrr_at_5 value: 95.494 - type: ndcg_at_1 value: 93.65 - type: ndcg_at_10 value: 90.794 - type: ndcg_at_100 value: 92.88300000000001 - type: ndcg_at_1000 value: 93.144 - type: ndcg_at_3 value: 90.32 - type: ndcg_at_5 value: 89.242 - type: precision_at_1 value: 93.65 - type: precision_at_10 value: 42.9 - type: precision_at_100 value: 4.835 - type: precision_at_1000 value: 0.49 - type: precision_at_3 value: 80.85 - type: precision_at_5 value: 68.14 - type: recall_at_1 value: 27.772000000000002 - type: recall_at_10 value: 91.183 - type: recall_at_100 value: 98.219 - type: recall_at_1000 value: 99.55000000000001 - type: recall_at_3 value: 60.911 - type: recall_at_5 value: 78.31099999999999 - type: main_score value: 90.794 task: type: Retrieval - dataset: config: default name: MTEB EcomRetrieval revision: None split: dev type: C-MTEB/EcomRetrieval metrics: - type: map_at_1 value: 54.6 - type: map_at_10 value: 64.742 - type: map_at_100 value: 65.289 - type: map_at_1000 value: 65.29700000000001 - type: map_at_3 value: 62.183 - type: map_at_5 value: 63.883 - type: mrr_at_1 value: 54.6 - type: mrr_at_10 value: 64.742 - type: mrr_at_100 value: 65.289 - type: mrr_at_1000 value: 65.29700000000001 - type: mrr_at_3 value: 62.183 - type: mrr_at_5 value: 63.883 - type: ndcg_at_1 value: 54.6 - type: ndcg_at_10 value: 69.719 - type: ndcg_at_100 value: 72.148 - type: ndcg_at_1000 value: 72.393 - type: ndcg_at_3 value: 64.606 - type: ndcg_at_5 value: 67.682 - type: precision_at_1 value: 54.6 - type: precision_at_10 value: 8.53 - type: precision_at_100 value: 0.962 - type: precision_at_1000 value: 0.098 - type: precision_at_3 value: 23.867 - type: precision_at_5 value: 15.82 - type: recall_at_1 value: 54.6 - type: recall_at_10 value: 85.3 - type: recall_at_100 value: 96.2 - type: recall_at_1000 value: 98.2 - type: recall_at_3 value: 71.6 - type: recall_at_5 value: 79.10000000000001 - type: main_score value: 69.719 task: type: Retrieval - dataset: config: default name: MTEB MMarcoReranking revision: None split: dev type: C-MTEB/Mmarco-reranking metrics: - type: map value: 35.30260957061897 - type: mrr value: 34.098015873015875 - type: main_score value: 35.30260957061897 task: type: Reranking - dataset: config: default name: MTEB MMarcoRetrieval revision: None split: dev type: C-MTEB/MMarcoRetrieval metrics: - type: map_at_1 value: 69.51899999999999 - type: map_at_10 value: 78.816 - type: map_at_100 value: 79.08500000000001 - type: map_at_1000 value: 79.091 - type: map_at_3 value: 76.999 - type: map_at_5 value: 78.194 - type: mrr_at_1 value: 71.80499999999999 - type: mrr_at_10 value: 79.29899999999999 - type: mrr_at_100 value: 79.532 - type: mrr_at_1000 value: 79.537 - type: mrr_at_3 value: 77.703 - type: mrr_at_5 value: 78.75999999999999 - type: ndcg_at_1 value: 71.80499999999999 - type: ndcg_at_10 value: 82.479 - type: ndcg_at_100 value: 83.611 - type: ndcg_at_1000 value: 83.76400000000001 - type: ndcg_at_3 value: 79.065 - type: ndcg_at_5 value: 81.092 - type: precision_at_1 value: 71.80499999999999 - type: precision_at_10 value: 9.91 - type: precision_at_100 value: 1.046 - type: precision_at_1000 value: 0.106 - type: precision_at_3 value: 29.727999999999998 - type: precision_at_5 value: 18.908 - type: recall_at_1 value: 69.51899999999999 - type: recall_at_10 value: 93.24 - type: recall_at_100 value: 98.19099999999999 - type: recall_at_1000 value: 99.36500000000001 - type: recall_at_3 value: 84.308 - type: recall_at_5 value: 89.119 - type: main_score value: 82.479 task: type: Retrieval - dataset: config: default name: MTEB MedicalRetrieval revision: None split: dev type: C-MTEB/MedicalRetrieval metrics: - type: map_at_1 value: 57.8 - type: map_at_10 value: 64.215 - type: map_at_100 value: 64.78 - type: map_at_1000 value: 64.81099999999999 - type: map_at_3 value: 62.64999999999999 - type: map_at_5 value: 63.57000000000001 - type: mrr_at_1 value: 58.099999999999994 - type: mrr_at_10 value: 64.371 - type: mrr_at_100 value: 64.936 - type: mrr_at_1000 value: 64.96600000000001 - type: mrr_at_3 value: 62.8 - type: mrr_at_5 value: 63.739999999999995 - type: ndcg_at_1 value: 57.8 - type: ndcg_at_10 value: 67.415 - type: ndcg_at_100 value: 70.38799999999999 - type: ndcg_at_1000 value: 71.229 - type: ndcg_at_3 value: 64.206 - type: ndcg_at_5 value: 65.858 - type: precision_at_1 value: 57.8 - type: precision_at_10 value: 7.75 - type: precision_at_100 value: 0.919 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 22.900000000000002 - type: precision_at_5 value: 14.540000000000001 - type: recall_at_1 value: 57.8 - type: recall_at_10 value: 77.5 - type: recall_at_100 value: 91.9 - type: recall_at_1000 value: 98.6 - type: recall_at_3 value: 68.7 - type: recall_at_5 value: 72.7 - type: main_score value: 67.415 task: type: Retrieval - dataset: config: default name: MTEB T2Reranking revision: None split: dev type: C-MTEB/T2Reranking metrics: - type: map value: 69.06615146698508 - type: mrr value: 79.7588755091294 - type: main_score value: 69.06615146698508 task: type: Reranking - dataset: config: default name: MTEB T2Retrieval revision: None split: dev type: C-MTEB/T2Retrieval metrics: - type: map_at_1 value: 28.084999999999997 - type: map_at_10 value: 78.583 - type: map_at_100 value: 82.14399999999999 - type: map_at_1000 value: 82.204 - type: map_at_3 value: 55.422000000000004 - type: map_at_5 value: 67.973 - type: mrr_at_1 value: 91.014 - type: mrr_at_10 value: 93.381 - type: mrr_at_100 value: 93.45400000000001 - type: mrr_at_1000 value: 93.45599999999999 - type: mrr_at_3 value: 92.99300000000001 - type: mrr_at_5 value: 93.234 - type: ndcg_at_1 value: 91.014 - type: ndcg_at_10 value: 85.931 - type: ndcg_at_100 value: 89.31 - type: ndcg_at_1000 value: 89.869 - type: ndcg_at_3 value: 87.348 - type: ndcg_at_5 value: 85.929 - type: precision_at_1 value: 91.014 - type: precision_at_10 value: 42.495 - type: precision_at_100 value: 5.029999999999999 - type: precision_at_1000 value: 0.516 - type: precision_at_3 value: 76.248 - type: precision_at_5 value: 63.817 - type: recall_at_1 value: 28.084999999999997 - type: recall_at_10 value: 84.88 - type: recall_at_100 value: 95.902 - type: recall_at_1000 value: 98.699 - type: recall_at_3 value: 57.113 - type: recall_at_5 value: 71.251 - type: main_score value: 85.931 task: type: Retrieval - dataset: config: default name: MTEB VideoRetrieval revision: None split: dev type: C-MTEB/VideoRetrieval metrics: - type: map_at_1 value: 66.4 - type: map_at_10 value: 75.86 - type: map_at_100 value: 76.185 - type: map_at_1000 value: 76.188 - type: map_at_3 value: 74.167 - type: map_at_5 value: 75.187 - type: mrr_at_1 value: 66.4 - type: mrr_at_10 value: 75.86 - type: mrr_at_100 value: 76.185 - type: mrr_at_1000 value: 76.188 - type: mrr_at_3 value: 74.167 - type: mrr_at_5 value: 75.187 - type: ndcg_at_1 value: 66.4 - type: ndcg_at_10 value: 80.03099999999999 - type: ndcg_at_100 value: 81.459 - type: ndcg_at_1000 value: 81.527 - type: ndcg_at_3 value: 76.621 - type: ndcg_at_5 value: 78.446 - type: precision_at_1 value: 66.4 - type: precision_at_10 value: 9.29 - type: precision_at_100 value: 0.992 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 27.900000000000002 - type: precision_at_5 value: 17.62 - type: recall_at_1 value: 66.4 - type: recall_at_10 value: 92.9 - type: recall_at_100 value: 99.2 - type: recall_at_1000 value: 99.7 - type: recall_at_3 value: 83.7 - type: recall_at_5 value: 88.1 - type: main_score value: 80.03099999999999 task: type: Retrieval pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb --- - # XYZ-embedding-zh This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1792 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('fangxq/XYZ-embedding-zh') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Dense({'in_features': 1024, 'out_features': 1792, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'}) ) ``` ## Citing & Authors