README.md

---
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.

<!--- Describe your model here -->

## 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

<!--- Describe how your model was evaluated -->

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