metadata
base_model: aspire/acge_text_embedding
pipeline_tag: sentence-similarity
tags:
- mteb
- sentence-transformers
- feature-extraction
- sentence-similarity
- llama-cpp
- gguf-my-repo
model-index:
- name: acge_text_embedding
results:
- task:
type: STS
dataset:
name: MTEB AFQMC
type: C-MTEB/AFQMC
config: default
split: validation
revision: b44c3b011063adb25877c13823db83bb193913c4
metrics:
- type: cos_sim_pearson
value: 54.03434872650919
- type: cos_sim_spearman
value: 58.80730796688325
- type: euclidean_pearson
value: 57.47231387497989
- type: euclidean_spearman
value: 58.80775026351807
- type: manhattan_pearson
value: 57.46332720141574
- type: manhattan_spearman
value: 58.80196022940078
- task:
type: STS
dataset:
name: MTEB ATEC
type: C-MTEB/ATEC
config: default
split: test
revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
metrics:
- type: cos_sim_pearson
value: 53.52621290548175
- type: cos_sim_spearman
value: 57.945227768312144
- type: euclidean_pearson
value: 61.17041394151802
- type: euclidean_spearman
value: 57.94553287835657
- type: manhattan_pearson
value: 61.168327500057885
- type: manhattan_spearman
value: 57.94477516925043
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (zh)
type: mteb/amazon_reviews_multi
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 48.538000000000004
- type: f1
value: 46.59920995594044
- task:
type: STS
dataset:
name: MTEB BQ
type: C-MTEB/BQ
config: default
split: test
revision: e3dda5e115e487b39ec7e618c0c6a29137052a55
metrics:
- type: cos_sim_pearson
value: 68.27529991817154
- type: cos_sim_spearman
value: 70.37095914176643
- type: euclidean_pearson
value: 69.42690712802727
- type: euclidean_spearman
value: 70.37017971889912
- type: manhattan_pearson
value: 69.40264877917839
- type: manhattan_spearman
value: 70.34786744049524
- task:
type: Clustering
dataset:
name: MTEB CLSClusteringP2P
type: C-MTEB/CLSClusteringP2P
config: default
split: test
revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476
metrics:
- type: v_measure
value: 47.08027536192709
- task:
type: Clustering
dataset:
name: MTEB CLSClusteringS2S
type: C-MTEB/CLSClusteringS2S
config: default
split: test
revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f
metrics:
- type: v_measure
value: 44.0526024940363
- task:
type: Reranking
dataset:
name: MTEB CMedQAv1
type: C-MTEB/CMedQAv1-reranking
config: default
split: test
revision: 8d7f1e942507dac42dc58017c1a001c3717da7df
metrics:
- type: map
value: 88.65974993133156
- type: mrr
value: 90.64761904761905
- task:
type: Reranking
dataset:
name: MTEB CMedQAv2
type: C-MTEB/CMedQAv2-reranking
config: default
split: test
revision: 23d186750531a14a0357ca22cd92d712fd512ea0
metrics:
- type: map
value: 88.90396838907245
- type: mrr
value: 90.90932539682541
- task:
type: Retrieval
dataset:
name: MTEB CmedqaRetrieval
type: C-MTEB/CmedqaRetrieval
config: default
split: dev
revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301
metrics:
- type: map_at_1
value: 26.875
- type: map_at_10
value: 39.995999999999995
- type: map_at_100
value: 41.899
- type: map_at_1000
value: 42
- type: map_at_3
value: 35.414
- type: map_at_5
value: 38.019
- type: mrr_at_1
value: 40.635
- type: mrr_at_10
value: 48.827
- type: mrr_at_100
value: 49.805
- type: mrr_at_1000
value: 49.845
- type: mrr_at_3
value: 46.145
- type: mrr_at_5
value: 47.693999999999996
- type: ndcg_at_1
value: 40.635
- type: ndcg_at_10
value: 46.78
- type: ndcg_at_100
value: 53.986999999999995
- type: ndcg_at_1000
value: 55.684
- type: ndcg_at_3
value: 41.018
- type: ndcg_at_5
value: 43.559
- type: precision_at_1
value: 40.635
- type: precision_at_10
value: 10.427999999999999
- type: precision_at_100
value: 1.625
- type: precision_at_1000
value: 0.184
- type: precision_at_3
value: 23.139000000000003
- type: precision_at_5
value: 17.004
- type: recall_at_1
value: 26.875
- type: recall_at_10
value: 57.887
- type: recall_at_100
value: 87.408
- type: recall_at_1000
value: 98.721
- type: recall_at_3
value: 40.812
- type: recall_at_5
value: 48.397
- task:
type: PairClassification
dataset:
name: MTEB Cmnli
type: C-MTEB/CMNLI
config: default
split: validation
revision: 41bc36f332156f7adc9e38f53777c959b2ae9766
metrics:
- type: cos_sim_accuracy
value: 83.43956704750451
- type: cos_sim_ap
value: 90.49172854352659
- type: cos_sim_f1
value: 84.28475486903963
- type: cos_sim_precision
value: 80.84603822203135
- type: cos_sim_recall
value: 88.02899228431144
- type: dot_accuracy
value: 83.43956704750451
- type: dot_ap
value: 90.46317132695233
- type: dot_f1
value: 84.28794294628929
- type: dot_precision
value: 80.51948051948052
- type: dot_recall
value: 88.4264671498714
- type: euclidean_accuracy
value: 83.43956704750451
- type: euclidean_ap
value: 90.49171785256486
- type: euclidean_f1
value: 84.28235820561584
- type: euclidean_precision
value: 80.8022308022308
- type: euclidean_recall
value: 88.07575403320084
- type: manhattan_accuracy
value: 83.55983162958509
- type: manhattan_ap
value: 90.48046779812815
- type: manhattan_f1
value: 84.45354259069714
- type: manhattan_precision
value: 82.21877767936226
- type: manhattan_recall
value: 86.81318681318682
- type: max_accuracy
value: 83.55983162958509
- type: max_ap
value: 90.49172854352659
- type: max_f1
value: 84.45354259069714
- task:
type: Retrieval
dataset:
name: MTEB CovidRetrieval
type: C-MTEB/CovidRetrieval
config: default
split: dev
revision: 1271c7809071a13532e05f25fb53511ffce77117
metrics:
- type: map_at_1
value: 68.54599999999999
- type: map_at_10
value: 77.62400000000001
- type: map_at_100
value: 77.886
- type: map_at_1000
value: 77.89
- type: map_at_3
value: 75.966
- type: map_at_5
value: 76.995
- type: mrr_at_1
value: 68.915
- type: mrr_at_10
value: 77.703
- type: mrr_at_100
value: 77.958
- type: mrr_at_1000
value: 77.962
- type: mrr_at_3
value: 76.08
- type: mrr_at_5
value: 77.118
- type: ndcg_at_1
value: 68.809
- type: ndcg_at_10
value: 81.563
- type: ndcg_at_100
value: 82.758
- type: ndcg_at_1000
value: 82.864
- type: ndcg_at_3
value: 78.29
- type: ndcg_at_5
value: 80.113
- type: precision_at_1
value: 68.809
- type: precision_at_10
value: 9.463000000000001
- type: precision_at_100
value: 1.001
- type: precision_at_1000
value: 0.101
- type: precision_at_3
value: 28.486
- type: precision_at_5
value: 18.019
- type: recall_at_1
value: 68.54599999999999
- type: recall_at_10
value: 93.625
- type: recall_at_100
value: 99.05199999999999
- type: recall_at_1000
value: 99.895
- type: recall_at_3
value: 84.879
- type: recall_at_5
value: 89.252
- task:
type: Retrieval
dataset:
name: MTEB DuRetrieval
type: C-MTEB/DuRetrieval
config: default
split: dev
revision: a1a333e290fe30b10f3f56498e3a0d911a693ced
metrics:
- type: map_at_1
value: 25.653
- type: map_at_10
value: 79.105
- type: map_at_100
value: 81.902
- type: map_at_1000
value: 81.947
- type: map_at_3
value: 54.54599999999999
- type: map_at_5
value: 69.226
- type: mrr_at_1
value: 89.35
- type: mrr_at_10
value: 92.69
- type: mrr_at_100
value: 92.77
- type: mrr_at_1000
value: 92.774
- type: mrr_at_3
value: 92.425
- type: mrr_at_5
value: 92.575
- type: ndcg_at_1
value: 89.35
- type: ndcg_at_10
value: 86.55199999999999
- type: ndcg_at_100
value: 89.35300000000001
- type: ndcg_at_1000
value: 89.782
- type: ndcg_at_3
value: 85.392
- type: ndcg_at_5
value: 84.5
- type: precision_at_1
value: 89.35
- type: precision_at_10
value: 41.589999999999996
- type: precision_at_100
value: 4.781
- type: precision_at_1000
value: 0.488
- type: precision_at_3
value: 76.683
- type: precision_at_5
value: 65.06
- type: recall_at_1
value: 25.653
- type: recall_at_10
value: 87.64999999999999
- type: recall_at_100
value: 96.858
- type: recall_at_1000
value: 99.13300000000001
- type: recall_at_3
value: 56.869
- type: recall_at_5
value: 74.024
- task:
type: Retrieval
dataset:
name: MTEB EcomRetrieval
type: C-MTEB/EcomRetrieval
config: default
split: dev
revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9
metrics:
- type: map_at_1
value: 52.1
- type: map_at_10
value: 62.629999999999995
- type: map_at_100
value: 63.117000000000004
- type: map_at_1000
value: 63.134
- type: map_at_3
value: 60.267
- type: map_at_5
value: 61.777
- type: mrr_at_1
value: 52.1
- type: mrr_at_10
value: 62.629999999999995
- type: mrr_at_100
value: 63.117000000000004
- type: mrr_at_1000
value: 63.134
- type: mrr_at_3
value: 60.267
- type: mrr_at_5
value: 61.777
- type: ndcg_at_1
value: 52.1
- type: ndcg_at_10
value: 67.596
- type: ndcg_at_100
value: 69.95
- type: ndcg_at_1000
value: 70.33500000000001
- type: ndcg_at_3
value: 62.82600000000001
- type: ndcg_at_5
value: 65.546
- type: precision_at_1
value: 52.1
- type: precision_at_10
value: 8.309999999999999
- type: precision_at_100
value: 0.941
- type: precision_at_1000
value: 0.097
- type: precision_at_3
value: 23.400000000000002
- type: precision_at_5
value: 15.36
- type: recall_at_1
value: 52.1
- type: recall_at_10
value: 83.1
- type: recall_at_100
value: 94.1
- type: recall_at_1000
value: 97
- type: recall_at_3
value: 70.19999999999999
- type: recall_at_5
value: 76.8
- task:
type: Classification
dataset:
name: MTEB IFlyTek
type: C-MTEB/IFlyTek-classification
config: default
split: validation
revision: 421605374b29664c5fc098418fe20ada9bd55f8a
metrics:
- type: accuracy
value: 51.773759138130046
- type: f1
value: 40.341407912920054
- task:
type: Classification
dataset:
name: MTEB JDReview
type: C-MTEB/JDReview-classification
config: default
split: test
revision: b7c64bd89eb87f8ded463478346f76731f07bf8b
metrics:
- type: accuracy
value: 86.69793621013133
- type: ap
value: 55.46718958939327
- type: f1
value: 81.48228915952436
- task:
type: STS
dataset:
name: MTEB LCQMC
type: C-MTEB/LCQMC
config: default
split: test
revision: 17f9b096f80380fce5ed12a9be8be7784b337daf
metrics:
- type: cos_sim_pearson
value: 71.1397780205448
- type: cos_sim_spearman
value: 78.17368193033309
- type: euclidean_pearson
value: 77.4849177602368
- type: euclidean_spearman
value: 78.17369079663212
- type: manhattan_pearson
value: 77.47344305182406
- type: manhattan_spearman
value: 78.16454335155387
- task:
type: Reranking
dataset:
name: MTEB MMarcoReranking
type: C-MTEB/Mmarco-reranking
config: default
split: dev
revision: 8e0c766dbe9e16e1d221116a3f36795fbade07f6
metrics:
- type: map
value: 27.76160559006673
- type: mrr
value: 28.02420634920635
- task:
type: Retrieval
dataset:
name: MTEB MMarcoRetrieval
type: C-MTEB/MMarcoRetrieval
config: default
split: dev
revision: 539bbde593d947e2a124ba72651aafc09eb33fc2
metrics:
- type: map_at_1
value: 65.661
- type: map_at_10
value: 74.752
- type: map_at_100
value: 75.091
- type: map_at_1000
value: 75.104
- type: map_at_3
value: 72.997
- type: map_at_5
value: 74.119
- type: mrr_at_1
value: 67.923
- type: mrr_at_10
value: 75.376
- type: mrr_at_100
value: 75.673
- type: mrr_at_1000
value: 75.685
- type: mrr_at_3
value: 73.856
- type: mrr_at_5
value: 74.82799999999999
- type: ndcg_at_1
value: 67.923
- type: ndcg_at_10
value: 78.424
- type: ndcg_at_100
value: 79.95100000000001
- type: ndcg_at_1000
value: 80.265
- type: ndcg_at_3
value: 75.101
- type: ndcg_at_5
value: 76.992
- type: precision_at_1
value: 67.923
- type: precision_at_10
value: 9.474
- type: precision_at_100
value: 1.023
- type: precision_at_1000
value: 0.105
- type: precision_at_3
value: 28.319
- type: precision_at_5
value: 17.986
- type: recall_at_1
value: 65.661
- type: recall_at_10
value: 89.09899999999999
- type: recall_at_100
value: 96.023
- type: recall_at_1000
value: 98.455
- type: recall_at_3
value: 80.314
- type: recall_at_5
value: 84.81
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (zh-CN)
type: mteb/amazon_massive_intent
config: zh-CN
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 75.86751849361131
- type: f1
value: 73.04918450508
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (zh-CN)
type: mteb/amazon_massive_scenario
config: zh-CN
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 78.4364492266308
- type: f1
value: 78.120686034844
- task:
type: Retrieval
dataset:
name: MTEB MedicalRetrieval
type: C-MTEB/MedicalRetrieval
config: default
split: dev
revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6
metrics:
- type: map_at_1
value: 55.00000000000001
- type: map_at_10
value: 61.06399999999999
- type: map_at_100
value: 61.622
- type: map_at_1000
value: 61.663000000000004
- type: map_at_3
value: 59.583
- type: map_at_5
value: 60.373
- type: mrr_at_1
value: 55.2
- type: mrr_at_10
value: 61.168
- type: mrr_at_100
value: 61.726000000000006
- type: mrr_at_1000
value: 61.767
- type: mrr_at_3
value: 59.683
- type: mrr_at_5
value: 60.492999999999995
- type: ndcg_at_1
value: 55.00000000000001
- type: ndcg_at_10
value: 64.098
- type: ndcg_at_100
value: 67.05
- type: ndcg_at_1000
value: 68.262
- type: ndcg_at_3
value: 61.00600000000001
- type: ndcg_at_5
value: 62.439
- type: precision_at_1
value: 55.00000000000001
- type: precision_at_10
value: 7.37
- type: precision_at_100
value: 0.881
- type: precision_at_1000
value: 0.098
- type: precision_at_3
value: 21.7
- type: precision_at_5
value: 13.719999999999999
- type: recall_at_1
value: 55.00000000000001
- type: recall_at_10
value: 73.7
- type: recall_at_100
value: 88.1
- type: recall_at_1000
value: 97.8
- type: recall_at_3
value: 65.10000000000001
- type: recall_at_5
value: 68.60000000000001
- task:
type: Classification
dataset:
name: MTEB MultilingualSentiment
type: C-MTEB/MultilingualSentiment-classification
config: default
split: validation
revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
metrics:
- type: accuracy
value: 77.52666666666667
- type: f1
value: 77.49784731367215
- task:
type: PairClassification
dataset:
name: MTEB Ocnli
type: C-MTEB/OCNLI
config: default
split: validation
revision: 66e76a618a34d6d565d5538088562851e6daa7ec
metrics:
- type: cos_sim_accuracy
value: 81.10449377368705
- type: cos_sim_ap
value: 85.17742765935606
- type: cos_sim_f1
value: 83.00094966761633
- type: cos_sim_precision
value: 75.40983606557377
- type: cos_sim_recall
value: 92.29144667370645
- type: dot_accuracy
value: 81.10449377368705
- type: dot_ap
value: 85.17143850809614
- type: dot_f1
value: 83.01707779886148
- type: dot_precision
value: 75.36606373815677
- type: dot_recall
value: 92.39704329461456
- type: euclidean_accuracy
value: 81.10449377368705
- type: euclidean_ap
value: 85.17856775343333
- type: euclidean_f1
value: 83.00094966761633
- type: euclidean_precision
value: 75.40983606557377
- type: euclidean_recall
value: 92.29144667370645
- type: manhattan_accuracy
value: 81.05035192203573
- type: manhattan_ap
value: 85.14464459395809
- type: manhattan_f1
value: 82.96155671570953
- type: manhattan_precision
value: 75.3448275862069
- type: manhattan_recall
value: 92.29144667370645
- type: max_accuracy
value: 81.10449377368705
- type: max_ap
value: 85.17856775343333
- type: max_f1
value: 83.01707779886148
- task:
type: Classification
dataset:
name: MTEB OnlineShopping
type: C-MTEB/OnlineShopping-classification
config: default
split: test
revision: e610f2ebd179a8fda30ae534c3878750a96db120
metrics:
- type: accuracy
value: 93.71000000000001
- type: ap
value: 91.83202232349356
- type: f1
value: 93.69900560334331
- task:
type: STS
dataset:
name: MTEB PAWSX
type: C-MTEB/PAWSX
config: default
split: test
revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1
metrics:
- type: cos_sim_pearson
value: 39.175047651512415
- type: cos_sim_spearman
value: 45.51434675777896
- type: euclidean_pearson
value: 44.864110004132286
- type: euclidean_spearman
value: 45.516433048896076
- type: manhattan_pearson
value: 44.87153627706517
- type: manhattan_spearman
value: 45.52862617925012
- task:
type: STS
dataset:
name: MTEB QBQTC
type: C-MTEB/QBQTC
config: default
split: test
revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7
metrics:
- type: cos_sim_pearson
value: 34.249579701429084
- type: cos_sim_spearman
value: 37.30903127368978
- type: euclidean_pearson
value: 35.129438425253355
- type: euclidean_spearman
value: 37.308544018709085
- type: manhattan_pearson
value: 35.08936153503652
- type: manhattan_spearman
value: 37.25582901077839
- task:
type: STS
dataset:
name: MTEB STS22 (zh)
type: mteb/sts22-crosslingual-sts
config: zh
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 61.29309637460004
- type: cos_sim_spearman
value: 65.85136090376717
- type: euclidean_pearson
value: 64.04783990953557
- type: euclidean_spearman
value: 65.85036859610366
- type: manhattan_pearson
value: 63.995852552712186
- type: manhattan_spearman
value: 65.86508416749417
- task:
type: STS
dataset:
name: MTEB STSB
type: C-MTEB/STSB
config: default
split: test
revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0
metrics:
- type: cos_sim_pearson
value: 81.5595940455587
- type: cos_sim_spearman
value: 82.72654634579749
- type: euclidean_pearson
value: 82.4892721061365
- type: euclidean_spearman
value: 82.72678504228253
- type: manhattan_pearson
value: 82.4770861422454
- type: manhattan_spearman
value: 82.71137469783162
- task:
type: Reranking
dataset:
name: MTEB T2Reranking
type: C-MTEB/T2Reranking
config: default
split: dev
revision: 76631901a18387f85eaa53e5450019b87ad58ef9
metrics:
- type: map
value: 66.6159547610527
- type: mrr
value: 76.35739406347057
- task:
type: Retrieval
dataset:
name: MTEB T2Retrieval
type: C-MTEB/T2Retrieval
config: default
split: dev
revision: 8731a845f1bf500a4f111cf1070785c793d10e64
metrics:
- type: map_at_1
value: 27.878999999999998
- type: map_at_10
value: 77.517
- type: map_at_100
value: 81.139
- type: map_at_1000
value: 81.204
- type: map_at_3
value: 54.728
- type: map_at_5
value: 67.128
- type: mrr_at_1
value: 90.509
- type: mrr_at_10
value: 92.964
- type: mrr_at_100
value: 93.045
- type: mrr_at_1000
value: 93.048
- type: mrr_at_3
value: 92.551
- type: mrr_at_5
value: 92.81099999999999
- type: ndcg_at_1
value: 90.509
- type: ndcg_at_10
value: 85.075
- type: ndcg_at_100
value: 88.656
- type: ndcg_at_1000
value: 89.25699999999999
- type: ndcg_at_3
value: 86.58200000000001
- type: ndcg_at_5
value: 85.138
- type: precision_at_1
value: 90.509
- type: precision_at_10
value: 42.05
- type: precision_at_100
value: 5.013999999999999
- type: precision_at_1000
value: 0.516
- type: precision_at_3
value: 75.551
- type: precision_at_5
value: 63.239999999999995
- type: recall_at_1
value: 27.878999999999998
- type: recall_at_10
value: 83.941
- type: recall_at_100
value: 95.568
- type: recall_at_1000
value: 98.55000000000001
- type: recall_at_3
value: 56.374
- type: recall_at_5
value: 70.435
- task:
type: Classification
dataset:
name: MTEB TNews
type: C-MTEB/TNews-classification
config: default
split: validation
revision: 317f262bf1e6126357bbe89e875451e4b0938fe4
metrics:
- type: accuracy
value: 53.687
- type: f1
value: 51.86911933364655
- task:
type: Clustering
dataset:
name: MTEB ThuNewsClusteringP2P
type: C-MTEB/ThuNewsClusteringP2P
config: default
split: test
revision: 5798586b105c0434e4f0fe5e767abe619442cf93
metrics:
- type: v_measure
value: 74.65887489872564
- task:
type: Clustering
dataset:
name: MTEB ThuNewsClusteringS2S
type: C-MTEB/ThuNewsClusteringS2S
config: default
split: test
revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d
metrics:
- type: v_measure
value: 69.00410995984436
- task:
type: Retrieval
dataset:
name: MTEB VideoRetrieval
type: C-MTEB/VideoRetrieval
config: default
split: dev
revision: 58c2597a5943a2ba48f4668c3b90d796283c5639
metrics:
- type: map_at_1
value: 59.4
- type: map_at_10
value: 69.214
- type: map_at_100
value: 69.72699999999999
- type: map_at_1000
value: 69.743
- type: map_at_3
value: 67.717
- type: map_at_5
value: 68.782
- type: mrr_at_1
value: 59.4
- type: mrr_at_10
value: 69.214
- type: mrr_at_100
value: 69.72699999999999
- type: mrr_at_1000
value: 69.743
- type: mrr_at_3
value: 67.717
- type: mrr_at_5
value: 68.782
- type: ndcg_at_1
value: 59.4
- type: ndcg_at_10
value: 73.32300000000001
- type: ndcg_at_100
value: 75.591
- type: ndcg_at_1000
value: 75.98700000000001
- type: ndcg_at_3
value: 70.339
- type: ndcg_at_5
value: 72.246
- type: precision_at_1
value: 59.4
- type: precision_at_10
value: 8.59
- type: precision_at_100
value: 0.96
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 25.967000000000002
- type: precision_at_5
value: 16.5
- type: recall_at_1
value: 59.4
- type: recall_at_10
value: 85.9
- type: recall_at_100
value: 96
- type: recall_at_1000
value: 99.1
- type: recall_at_3
value: 77.9
- type: recall_at_5
value: 82.5
- task:
type: Classification
dataset:
name: MTEB Waimai
type: C-MTEB/waimai-classification
config: default
split: test
revision: 339287def212450dcaa9df8c22bf93e9980c7023
metrics:
- type: accuracy
value: 88.53
- type: ap
value: 73.56216166534062
- type: f1
value: 87.06093694294485
bnightning/acge_text_embedding-Q4_K_M-GGUF
This model was converted to GGUF format from aspire/acge_text_embedding using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card for more details on the model.
Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
brew install llama.cpp
Invoke the llama.cpp server or the CLI.
CLI:
llama-cli --hf-repo bnightning/acge_text_embedding-Q4_K_M-GGUF --hf-file acge_text_embedding-q4_k_m.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo bnightning/acge_text_embedding-Q4_K_M-GGUF --hf-file acge_text_embedding-q4_k_m.gguf -c 2048
Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
git clone https://github.com/ggerganov/llama.cpp
Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
cd llama.cpp && LLAMA_CURL=1 make
Step 3: Run inference through the main binary.
./llama-cli --hf-repo bnightning/acge_text_embedding-Q4_K_M-GGUF --hf-file acge_text_embedding-q4_k_m.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo bnightning/acge_text_embedding-Q4_K_M-GGUF --hf-file acge_text_embedding-q4_k_m.gguf -c 2048