metadata
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
- 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 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 installed:
pip install -U sentence-transformers
Then you can use the model like this:
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
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'})
)