---
model-index:
- name: XYZ-embedding
  results:
  - dataset:
      config: default
      name: MTEB CmedqaRetrieval
      revision: None
      split: dev
      type: C-MTEB/CmedqaRetrieval
    metrics:
    - type: map_at_1
      value: 27.796
    - type: map_at_10
      value: 41.498000000000005
    - type: map_at_100
      value: 43.332
    - type: map_at_1000
      value: 43.429
    - type: map_at_3
      value: 37.172
    - type: map_at_5
      value: 39.617000000000004
    - type: mrr_at_1
      value: 42.111
    - type: mrr_at_10
      value: 50.726000000000006
    - type: mrr_at_100
      value: 51.632
    - type: mrr_at_1000
      value: 51.67
    - type: mrr_at_3
      value: 48.429
    - type: mrr_at_5
      value: 49.662
    - type: ndcg_at_1
      value: 42.111
    - type: ndcg_at_10
      value: 48.294
    - type: ndcg_at_100
      value: 55.135999999999996
    - type: ndcg_at_1000
      value: 56.818000000000005
    - type: ndcg_at_3
      value: 43.185
    - type: ndcg_at_5
      value: 45.266
    - type: precision_at_1
      value: 42.111
    - type: precision_at_10
      value: 10.635
    - type: precision_at_100
      value: 1.619
    - type: precision_at_1000
      value: 0.183
    - type: precision_at_3
      value: 24.539
    - type: precision_at_5
      value: 17.644000000000002
    - type: recall_at_1
      value: 27.796
    - type: recall_at_10
      value: 59.034
    - type: recall_at_100
      value: 86.991
    - type: recall_at_1000
      value: 98.304
    - type: recall_at_3
      value: 43.356
    - type: recall_at_5
      value: 49.998
    - type: main_score
      value: 48.294
    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB CovidRetrieval
      revision: None
      split: dev
      type: C-MTEB/CovidRetrieval
    metrics:
    - type: map_at_1
      value: 80.479
    - type: map_at_10
      value: 87.984
    - type: map_at_100
      value: 88.036
    - type: map_at_1000
      value: 88.03699999999999
    - type: map_at_3
      value: 87.083
    - type: map_at_5
      value: 87.694
    - type: mrr_at_1
      value: 80.927
    - type: mrr_at_10
      value: 88.046
    - type: mrr_at_100
      value: 88.099
    - type: mrr_at_1000
      value: 88.1
    - type: mrr_at_3
      value: 87.215
    - type: mrr_at_5
      value: 87.768
    - type: ndcg_at_1
      value: 80.927
    - type: ndcg_at_10
      value: 90.756
    - type: ndcg_at_100
      value: 90.96
    - type: ndcg_at_1000
      value: 90.975
    - type: ndcg_at_3
      value: 89.032
    - type: ndcg_at_5
      value: 90.106
    - type: precision_at_1
      value: 80.927
    - type: precision_at_10
      value: 10.011000000000001
    - type: precision_at_100
      value: 1.009
    - type: precision_at_1000
      value: 0.101
    - type: precision_at_3
      value: 31.752999999999997
    - type: precision_at_5
      value: 19.6
    - type: recall_at_1
      value: 80.479
    - type: recall_at_10
      value: 99.05199999999999
    - type: recall_at_100
      value: 99.895
    - type: recall_at_1000
      value: 100.0
    - type: recall_at_3
      value: 94.494
    - type: recall_at_5
      value: 97.102
    - type: main_score
      value: 90.756
    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB DuRetrieval
      revision: None
      split: dev
      type: C-MTEB/DuRetrieval
    metrics:
    - type: map_at_1
      value: 27.853
    - type: map_at_10
      value: 85.13199999999999
    - type: map_at_100
      value: 87.688
    - type: map_at_1000
      value: 87.712
    - type: map_at_3
      value: 59.705
    - type: map_at_5
      value: 75.139
    - type: mrr_at_1
      value: 93.65
    - type: mrr_at_10
      value: 95.682
    - type: mrr_at_100
      value: 95.722
    - type: mrr_at_1000
      value: 95.724
    - type: mrr_at_3
      value: 95.467
    - type: mrr_at_5
      value: 95.612
    - type: ndcg_at_1
      value: 93.65
    - type: ndcg_at_10
      value: 91.155
    - type: ndcg_at_100
      value: 93.183
    - type: ndcg_at_1000
      value: 93.38499999999999
    - type: ndcg_at_3
      value: 90.648
    - type: ndcg_at_5
      value: 89.47699999999999
    - type: precision_at_1
      value: 93.65
    - type: precision_at_10
      value: 43.11
    - type: precision_at_100
      value: 4.854
    - type: precision_at_1000
      value: 0.49100000000000005
    - type: precision_at_3
      value: 81.11699999999999
    - type: precision_at_5
      value: 68.28999999999999
    - type: recall_at_1
      value: 27.853
    - type: recall_at_10
      value: 91.678
    - type: recall_at_100
      value: 98.553
    - type: recall_at_1000
      value: 99.553
    - type: recall_at_3
      value: 61.381
    - type: recall_at_5
      value: 78.605
    - type: main_score
      value: 91.155
    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB EcomRetrieval
      revision: None
      split: dev
      type: C-MTEB/EcomRetrieval
    metrics:
    - type: map_at_1
      value: 54.50000000000001
    - type: map_at_10
      value: 65.167
    - type: map_at_100
      value: 65.664
    - type: map_at_1000
      value: 65.67399999999999
    - type: map_at_3
      value: 62.633
    - type: map_at_5
      value: 64.208
    - type: mrr_at_1
      value: 54.50000000000001
    - type: mrr_at_10
      value: 65.167
    - type: mrr_at_100
      value: 65.664
    - type: mrr_at_1000
      value: 65.67399999999999
    - type: mrr_at_3
      value: 62.633
    - type: mrr_at_5
      value: 64.208
    - type: ndcg_at_1
      value: 54.50000000000001
    - type: ndcg_at_10
      value: 70.294
    - type: ndcg_at_100
      value: 72.564
    - type: ndcg_at_1000
      value: 72.841
    - type: ndcg_at_3
      value: 65.128
    - type: ndcg_at_5
      value: 67.96799999999999
    - type: precision_at_1
      value: 54.50000000000001
    - type: precision_at_10
      value: 8.64
    - type: precision_at_100
      value: 0.967
    - type: precision_at_1000
      value: 0.099
    - type: precision_at_3
      value: 24.099999999999998
    - type: precision_at_5
      value: 15.840000000000002
    - type: recall_at_1
      value: 54.50000000000001
    - type: recall_at_10
      value: 86.4
    - type: recall_at_100
      value: 96.7
    - type: recall_at_1000
      value: 98.9
    - type: recall_at_3
      value: 72.3
    - type: recall_at_5
      value: 79.2
    - type: main_score
      value: 70.294
    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB MMarcoRetrieval
      revision: None
      split: dev
      type: C-MTEB/MMarcoRetrieval
    metrics:
    - type: map_at_1
      value: 69.401
    - type: map_at_10
      value: 78.8
    - type: map_at_100
      value: 79.077
    - type: map_at_1000
      value: 79.081
    - type: map_at_3
      value: 76.97
    - type: map_at_5
      value: 78.185
    - type: mrr_at_1
      value: 71.719
    - type: mrr_at_10
      value: 79.327
    - type: mrr_at_100
      value: 79.56400000000001
    - type: mrr_at_1000
      value: 79.56800000000001
    - type: mrr_at_3
      value: 77.736
    - type: mrr_at_5
      value: 78.782
    - type: ndcg_at_1
      value: 71.719
    - type: ndcg_at_10
      value: 82.505
    - type: ndcg_at_100
      value: 83.673
    - type: ndcg_at_1000
      value: 83.786
    - type: ndcg_at_3
      value: 79.07600000000001
    - type: ndcg_at_5
      value: 81.122
    - type: precision_at_1
      value: 71.719
    - type: precision_at_10
      value: 9.924
    - type: precision_at_100
      value: 1.049
    - type: precision_at_1000
      value: 0.106
    - type: precision_at_3
      value: 29.742
    - type: precision_at_5
      value: 18.937
    - type: recall_at_1
      value: 69.401
    - type: recall_at_10
      value: 93.349
    - type: recall_at_100
      value: 98.492
    - type: recall_at_1000
      value: 99.384
    - type: recall_at_3
      value: 84.385
    - type: recall_at_5
      value: 89.237
    - type: main_score
      value: 82.505
    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: 64.696
    - type: map_at_100
      value: 65.294
    - type: map_at_1000
      value: 65.328
    - type: map_at_3
      value: 62.949999999999996
    - type: map_at_5
      value: 64.095
    - type: mrr_at_1
      value: 58.099999999999994
    - type: mrr_at_10
      value: 64.85
    - type: mrr_at_100
      value: 65.448
    - type: mrr_at_1000
      value: 65.482
    - type: mrr_at_3
      value: 63.1
    - type: mrr_at_5
      value: 64.23
    - type: ndcg_at_1
      value: 57.8
    - type: ndcg_at_10
      value: 68.041
    - type: ndcg_at_100
      value: 71.074
    - type: ndcg_at_1000
      value: 71.919
    - type: ndcg_at_3
      value: 64.584
    - type: ndcg_at_5
      value: 66.625
    - type: precision_at_1
      value: 57.8
    - type: precision_at_10
      value: 7.85
    - type: precision_at_100
      value: 0.9289999999999999
    - type: precision_at_1000
      value: 0.099
    - type: precision_at_3
      value: 23.1
    - type: precision_at_5
      value: 14.84
    - type: recall_at_1
      value: 57.8
    - type: recall_at_10
      value: 78.5
    - type: recall_at_100
      value: 92.9
    - type: recall_at_1000
      value: 99.4
    - type: recall_at_3
      value: 69.3
    - type: recall_at_5
      value: 74.2
    - type: main_score
      value: 68.041
    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB T2Retrieval
      revision: None
      split: dev
      type: C-MTEB/T2Retrieval
    metrics:
    - type: map_at_1
      value: 28.041
    - type: map_at_10
      value: 78.509
    - type: map_at_100
      value: 82.083
    - type: map_at_1000
      value: 82.143
    - type: map_at_3
      value: 55.345
    - type: map_at_5
      value: 67.899
    - type: mrr_at_1
      value: 90.86
    - type: mrr_at_10
      value: 93.31
    - type: mrr_at_100
      value: 93.388
    - type: mrr_at_1000
      value: 93.391
    - type: mrr_at_3
      value: 92.92200000000001
    - type: mrr_at_5
      value: 93.167
    - type: ndcg_at_1
      value: 90.86
    - type: ndcg_at_10
      value: 85.875
    - type: ndcg_at_100
      value: 89.269
    - type: ndcg_at_1000
      value: 89.827
    - type: ndcg_at_3
      value: 87.254
    - type: ndcg_at_5
      value: 85.855
    - type: precision_at_1
      value: 90.86
    - type: precision_at_10
      value: 42.488
    - type: precision_at_100
      value: 5.029
    - type: precision_at_1000
      value: 0.516
    - type: precision_at_3
      value: 76.172
    - type: precision_at_5
      value: 63.759
    - type: recall_at_1
      value: 28.041
    - type: recall_at_10
      value: 84.829
    - type: recall_at_100
      value: 95.89999999999999
    - type: recall_at_1000
      value: 98.665
    - type: recall_at_3
      value: 57.009
    - type: recall_at_5
      value: 71.188
    - type: main_score
      value: 85.875
    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB VideoRetrieval
      revision: None
      split: dev
      type: C-MTEB/VideoRetrieval
    metrics:
    - type: map_at_1
      value: 67.30000000000001
    - type: map_at_10
      value: 76.819
    - type: map_at_100
      value: 77.141
    - type: map_at_1000
      value: 77.142
    - type: map_at_3
      value: 75.233
    - type: map_at_5
      value: 76.163
    - type: mrr_at_1
      value: 67.30000000000001
    - type: mrr_at_10
      value: 76.819
    - type: mrr_at_100
      value: 77.141
    - type: mrr_at_1000
      value: 77.142
    - type: mrr_at_3
      value: 75.233
    - type: mrr_at_5
      value: 76.163
    - type: ndcg_at_1
      value: 67.30000000000001
    - type: ndcg_at_10
      value: 80.93599999999999
    - type: ndcg_at_100
      value: 82.311
    - type: ndcg_at_1000
      value: 82.349
    - type: ndcg_at_3
      value: 77.724
    - type: ndcg_at_5
      value: 79.406
    - type: precision_at_1
      value: 67.30000000000001
    - type: precision_at_10
      value: 9.36
    - type: precision_at_100
      value: 0.996
    - type: precision_at_1000
      value: 0.1
    - type: precision_at_3
      value: 28.299999999999997
    - type: precision_at_5
      value: 17.8
    - type: recall_at_1
      value: 67.30000000000001
    - type: recall_at_10
      value: 93.60000000000001
    - type: recall_at_100
      value: 99.6
    - type: recall_at_1000
      value: 99.9
    - type: recall_at_3
      value: 84.89999999999999
    - type: recall_at_5
      value: 89.0
    - type: main_score
      value: 80.93599999999999
    task:
      type: Retrieval
tags:
- mteb
language:
- zh

---

<h2 align="left">XYZ-embedding</h2>

## Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("fangxq/XYZ-embedding")
# Run inference
sentences = [
    'The weather is lovely today.',
    "It's so sunny outside!",
    'He drove to the stadium.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1792]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```