Update README.md

README.md

@@ -12,2586 +12,100 @@ tags:
 - dementia
 - dementia disease
 language: en
-inference: false
+inference: true
 license: apache-2.0
-model-index:
-- name: INSTRUCTOR
-  results:
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_counterfactual
-      name: MTEB AmazonCounterfactualClassification (en)
-      config: en
-      split: test
-      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
-    metrics:
-    - type: accuracy
-      value: 88.13432835820896
-    - type: ap
-      value: 59.298209334395665
-    - type: f1
-      value: 83.31769058643586
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_polarity
-      name: MTEB AmazonPolarityClassification
-      config: default
-      split: test
-      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
-    metrics:
-    - type: accuracy
-      value: 91.526375
-    - type: ap
-      value: 88.16327709705504
-    - type: f1
-      value: 91.51095801287843
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_reviews_multi
-      name: MTEB AmazonReviewsClassification (en)
-      config: en
-      split: test
-      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
-    metrics:
-    - type: accuracy
-      value: 47.856
-    - type: f1
-      value: 45.41490917650942
-  - task:
-      type: Retrieval
-    dataset:
-      type: arguana
-      name: MTEB ArguAna
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 31.223
-    - type: map_at_10
-      value: 47.947
-    - type: map_at_100
-      value: 48.742000000000004
-    - type: map_at_1000
-      value: 48.745
-    - type: map_at_3
-      value: 43.137
-    - type: map_at_5
-      value: 45.992
-    - type: mrr_at_1
-      value: 32.432
-    - type: mrr_at_10
-      value: 48.4
-    - type: mrr_at_100
-      value: 49.202
-    - type: mrr_at_1000
-      value: 49.205
-    - type: mrr_at_3
-      value: 43.551
-    - type: mrr_at_5
-      value: 46.467999999999996
-    - type: ndcg_at_1
-      value: 31.223
-    - type: ndcg_at_10
-      value: 57.045
-    - type: ndcg_at_100
-      value: 60.175
-    - type: ndcg_at_1000
-      value: 60.233000000000004
-    - type: ndcg_at_3
-      value: 47.171
-    - type: ndcg_at_5
-      value: 52.322
-    - type: precision_at_1
-      value: 31.223
-    - type: precision_at_10
-      value: 8.599
-    - type: precision_at_100
-      value: 0.991
-    - type: precision_at_1000
-      value: 0.1
-    - type: precision_at_3
-      value: 19.63
-    - type: precision_at_5
-      value: 14.282
-    - type: recall_at_1
-      value: 31.223
-    - type: recall_at_10
-      value: 85.989
-    - type: recall_at_100
-      value: 99.075
-    - type: recall_at_1000
-      value: 99.502
-    - type: recall_at_3
-      value: 58.89
-    - type: recall_at_5
-      value: 71.408
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/arxiv-clustering-p2p
-      name: MTEB ArxivClusteringP2P
-      config: default
-      split: test
-      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
-    metrics:
-    - type: v_measure
-      value: 43.1621946393635
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/arxiv-clustering-s2s
-      name: MTEB ArxivClusteringS2S
-      config: default
-      split: test
-      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
-    metrics:
-    - type: v_measure
-      value: 32.56417132407894
-  - task:
-      type: Reranking
-    dataset:
-      type: mteb/askubuntudupquestions-reranking
-      name: MTEB AskUbuntuDupQuestions
-      config: default
-      split: test
-      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
-    metrics:
-    - type: map
-      value: 64.29539304390207
-    - type: mrr
-      value: 76.44484017060196
-  - task:
-      type: STS
-    dataset:
-      type: mteb/biosses-sts
-      name: MTEB BIOSSES
-      config: default
-      split: test
-      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
-    metrics:
-    - type: cos_sim_spearman
-      value: 84.38746499431112
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/banking77
-      name: MTEB Banking77Classification
-      config: default
-      split: test
-      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
-    metrics:
-    - type: accuracy
-      value: 78.51298701298701
-    - type: f1
-      value: 77.49041754069235
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/biorxiv-clustering-p2p
-      name: MTEB BiorxivClusteringP2P
-      config: default
-      split: test
-      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
-    metrics:
-    - type: v_measure
-      value: 37.61848554098577
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/biorxiv-clustering-s2s
-      name: MTEB BiorxivClusteringS2S
-      config: default
-      split: test
-      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
-    metrics:
-    - type: v_measure
-      value: 31.32623280148178
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackAndroidRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 35.803000000000004
-    - type: map_at_10
-      value: 48.848
-    - type: map_at_100
-      value: 50.5
-    - type: map_at_1000
-      value: 50.602999999999994
-    - type: map_at_3
-      value: 45.111000000000004
-    - type: map_at_5
-      value: 47.202
-    - type: mrr_at_1
-      value: 44.635000000000005
-    - type: mrr_at_10
-      value: 55.593
-    - type: mrr_at_100
-      value: 56.169999999999995
-    - type: mrr_at_1000
-      value: 56.19499999999999
-    - type: mrr_at_3
-      value: 53.361999999999995
-    - type: mrr_at_5
-      value: 54.806999999999995
-    - type: ndcg_at_1
-      value: 44.635000000000005
-    - type: ndcg_at_10
-      value: 55.899
-    - type: ndcg_at_100
-      value: 60.958
-    - type: ndcg_at_1000
-      value: 62.302
-    - type: ndcg_at_3
-      value: 51.051
-    - type: ndcg_at_5
-      value: 53.351000000000006
-    - type: precision_at_1
-      value: 44.635000000000005
-    - type: precision_at_10
-      value: 10.786999999999999
-    - type: precision_at_100
-      value: 1.6580000000000001
-    - type: precision_at_1000
-      value: 0.213
-    - type: precision_at_3
-      value: 24.893
-    - type: precision_at_5
-      value: 17.740000000000002
-    - type: recall_at_1
-      value: 35.803000000000004
-    - type: recall_at_10
-      value: 68.657
-    - type: recall_at_100
-      value: 89.77199999999999
-    - type: recall_at_1000
-      value: 97.67
-    - type: recall_at_3
-      value: 54.066
-    - type: recall_at_5
-      value: 60.788
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackEnglishRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 33.706
-    - type: map_at_10
-      value: 44.896
-    - type: map_at_100
-      value: 46.299
-    - type: map_at_1000
-      value: 46.44
-    - type: map_at_3
-      value: 41.721000000000004
-    - type: map_at_5
-      value: 43.486000000000004
-    - type: mrr_at_1
-      value: 41.592
-    - type: mrr_at_10
-      value: 50.529
-    - type: mrr_at_100
-      value: 51.22
-    - type: mrr_at_1000
-      value: 51.258
-    - type: mrr_at_3
-      value: 48.205999999999996
-    - type: mrr_at_5
-      value: 49.528
-    - type: ndcg_at_1
-      value: 41.592
-    - type: ndcg_at_10
-      value: 50.77199999999999
-    - type: ndcg_at_100
-      value: 55.383
-    - type: ndcg_at_1000
-      value: 57.288
-    - type: ndcg_at_3
-      value: 46.324
-    - type: ndcg_at_5
-      value: 48.346000000000004
-    - type: precision_at_1
-      value: 41.592
-    - type: precision_at_10
-      value: 9.516
-    - type: precision_at_100
-      value: 1.541
-    - type: precision_at_1000
-      value: 0.2
-    - type: precision_at_3
-      value: 22.399
-    - type: precision_at_5
-      value: 15.770999999999999
-    - type: recall_at_1
-      value: 33.706
-    - type: recall_at_10
-      value: 61.353
-    - type: recall_at_100
-      value: 80.182
-    - type: recall_at_1000
-      value: 91.896
-    - type: recall_at_3
-      value: 48.204
-    - type: recall_at_5
-      value: 53.89699999999999
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackGamingRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 44.424
-    - type: map_at_10
-      value: 57.169000000000004
-    - type: map_at_100
-      value: 58.202
-    - type: map_at_1000
-      value: 58.242000000000004
-    - type: map_at_3
-      value: 53.825
-    - type: map_at_5
-      value: 55.714
-    - type: mrr_at_1
-      value: 50.470000000000006
-    - type: mrr_at_10
-      value: 60.489000000000004
-    - type: mrr_at_100
-      value: 61.096
-    - type: mrr_at_1000
-      value: 61.112
-    - type: mrr_at_3
-      value: 58.192
-    - type: mrr_at_5
-      value: 59.611999999999995
-    - type: ndcg_at_1
-      value: 50.470000000000006
-    - type: ndcg_at_10
-      value: 63.071999999999996
-    - type: ndcg_at_100
-      value: 66.964
-    - type: ndcg_at_1000
-      value: 67.659
-    - type: ndcg_at_3
-      value: 57.74399999999999
-    - type: ndcg_at_5
-      value: 60.367000000000004
-    - type: precision_at_1
-      value: 50.470000000000006
-    - type: precision_at_10
-      value: 10.019
-    - type: precision_at_100
-      value: 1.29
-    - type: precision_at_1000
-      value: 0.13899999999999998
-    - type: precision_at_3
-      value: 25.558999999999997
-    - type: precision_at_5
-      value: 17.467
-    - type: recall_at_1
-      value: 44.424
-    - type: recall_at_10
-      value: 77.02
-    - type: recall_at_100
-      value: 93.738
-    - type: recall_at_1000
-      value: 98.451
-    - type: recall_at_3
-      value: 62.888
-    - type: recall_at_5
-      value: 69.138
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackGisRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 26.294
-    - type: map_at_10
-      value: 34.503
-    - type: map_at_100
-      value: 35.641
-    - type: map_at_1000
-      value: 35.724000000000004
-    - type: map_at_3
-      value: 31.753999999999998
-    - type: map_at_5
-      value: 33.190999999999995
-    - type: mrr_at_1
-      value: 28.362
-    - type: mrr_at_10
-      value: 36.53
-    - type: mrr_at_100
-      value: 37.541000000000004
-    - type: mrr_at_1000
-      value: 37.602000000000004
-    - type: mrr_at_3
-      value: 33.917
-    - type: mrr_at_5
-      value: 35.358000000000004
-    - type: ndcg_at_1
-      value: 28.362
-    - type: ndcg_at_10
-      value: 39.513999999999996
-    - type: ndcg_at_100
-      value: 44.815
-    - type: ndcg_at_1000
-      value: 46.839
-    - type: ndcg_at_3
-      value: 34.02
-    - type: ndcg_at_5
-      value: 36.522
-    - type: precision_at_1
-      value: 28.362
-    - type: precision_at_10
-      value: 6.101999999999999
-    - type: precision_at_100
-      value: 0.9129999999999999
-    - type: precision_at_1000
-      value: 0.11399999999999999
-    - type: precision_at_3
-      value: 14.161999999999999
-    - type: precision_at_5
-      value: 9.966
-    - type: recall_at_1
-      value: 26.294
-    - type: recall_at_10
-      value: 53.098
-    - type: recall_at_100
-      value: 76.877
-    - type: recall_at_1000
-      value: 91.834
-    - type: recall_at_3
-      value: 38.266
-    - type: recall_at_5
-      value: 44.287
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackMathematicaRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 16.407
-    - type: map_at_10
-      value: 25.185999999999996
-    - type: map_at_100
-      value: 26.533
-    - type: map_at_1000
-      value: 26.657999999999998
-    - type: map_at_3
-      value: 22.201999999999998
-    - type: map_at_5
-      value: 23.923
-    - type: mrr_at_1
-      value: 20.522000000000002
-    - type: mrr_at_10
-      value: 29.522
-    - type: mrr_at_100
-      value: 30.644
-    - type: mrr_at_1000
-      value: 30.713
-    - type: mrr_at_3
-      value: 26.679000000000002
-    - type: mrr_at_5
-      value: 28.483000000000004
-    - type: ndcg_at_1
-      value: 20.522000000000002
-    - type: ndcg_at_10
-      value: 30.656
-    - type: ndcg_at_100
-      value: 36.864999999999995
-    - type: ndcg_at_1000
-      value: 39.675
-    - type: ndcg_at_3
-      value: 25.319000000000003
-    - type: ndcg_at_5
-      value: 27.992
-    - type: precision_at_1
-      value: 20.522000000000002
-    - type: precision_at_10
-      value: 5.795999999999999
-    - type: precision_at_100
-      value: 1.027
-    - type: precision_at_1000
-      value: 0.13999999999999999
-    - type: precision_at_3
-      value: 12.396
-    - type: precision_at_5
-      value: 9.328
-    - type: recall_at_1
-      value: 16.407
-    - type: recall_at_10
-      value: 43.164
-    - type: recall_at_100
-      value: 69.695
-    - type: recall_at_1000
-      value: 89.41900000000001
-    - type: recall_at_3
-      value: 28.634999999999998
-    - type: recall_at_5
-      value: 35.308
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackPhysicsRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 30.473
-    - type: map_at_10
-      value: 41.676
-    - type: map_at_100
-      value: 43.120999999999995
-    - type: map_at_1000
-      value: 43.230000000000004
-    - type: map_at_3
-      value: 38.306000000000004
-    - type: map_at_5
-      value: 40.355999999999995
-    - type: mrr_at_1
-      value: 37.536
-    - type: mrr_at_10
-      value: 47.643
-    - type: mrr_at_100
-      value: 48.508
-    - type: mrr_at_1000
-      value: 48.551
-    - type: mrr_at_3
-      value: 45.348
-    - type: mrr_at_5
-      value: 46.744
-    - type: ndcg_at_1
-      value: 37.536
-    - type: ndcg_at_10
-      value: 47.823
-    - type: ndcg_at_100
-      value: 53.395
-    - type: ndcg_at_1000
-      value: 55.271
-    - type: ndcg_at_3
-      value: 42.768
-    - type: ndcg_at_5
-      value: 45.373000000000005
-    - type: precision_at_1
-      value: 37.536
-    - type: precision_at_10
-      value: 8.681
-    - type: precision_at_100
-      value: 1.34
-    - type: precision_at_1000
-      value: 0.165
-    - type: precision_at_3
-      value: 20.468
-    - type: precision_at_5
-      value: 14.495
-    - type: recall_at_1
-      value: 30.473
-    - type: recall_at_10
-      value: 60.092999999999996
-    - type: recall_at_100
-      value: 82.733
-    - type: recall_at_1000
-      value: 94.875
-    - type: recall_at_3
-      value: 45.734
-    - type: recall_at_5
-      value: 52.691
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackProgrammersRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 29.976000000000003
-    - type: map_at_10
-      value: 41.097
-    - type: map_at_100
-      value: 42.547000000000004
-    - type: map_at_1000
-      value: 42.659000000000006
-    - type: map_at_3
-      value: 37.251
-    - type: map_at_5
-      value: 39.493
-    - type: mrr_at_1
-      value: 37.557
-    - type: mrr_at_10
-      value: 46.605000000000004
-    - type: mrr_at_100
-      value: 47.487
-    - type: mrr_at_1000
-      value: 47.54
-    - type: mrr_at_3
-      value: 43.721
-    - type: mrr_at_5
-      value: 45.411
-    - type: ndcg_at_1
-      value: 37.557
-    - type: ndcg_at_10
-      value: 47.449000000000005
-    - type: ndcg_at_100
-      value: 53.052
-    - type: ndcg_at_1000
-      value: 55.010999999999996
-    - type: ndcg_at_3
-      value: 41.439
-    - type: ndcg_at_5
-      value: 44.292
-    - type: precision_at_1
-      value: 37.557
-    - type: precision_at_10
-      value: 8.847
-    - type: precision_at_100
-      value: 1.357
-    - type: precision_at_1000
-      value: 0.16999999999999998
-    - type: precision_at_3
-      value: 20.091
-    - type: precision_at_5
-      value: 14.384
-    - type: recall_at_1
-      value: 29.976000000000003
-    - type: recall_at_10
-      value: 60.99099999999999
-    - type: recall_at_100
-      value: 84.245
-    - type: recall_at_1000
-      value: 96.97200000000001
-    - type: recall_at_3
-      value: 43.794
-    - type: recall_at_5
-      value: 51.778999999999996
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 28.099166666666665
-    - type: map_at_10
-      value: 38.1365
-    - type: map_at_100
-      value: 39.44491666666667
-    - type: map_at_1000
-      value: 39.55858333333334
-    - type: map_at_3
-      value: 35.03641666666666
-    - type: map_at_5
-      value: 36.79833333333334
-    - type: mrr_at_1
-      value: 33.39966666666667
-    - type: mrr_at_10
-      value: 42.42583333333333
-    - type: mrr_at_100
-      value: 43.28575
-    - type: mrr_at_1000
-      value: 43.33741666666667
-    - type: mrr_at_3
-      value: 39.94975
-    - type: mrr_at_5
-      value: 41.41633333333334
-    - type: ndcg_at_1
-      value: 33.39966666666667
-    - type: ndcg_at_10
-      value: 43.81741666666667
-    - type: ndcg_at_100
-      value: 49.08166666666667
-    - type: ndcg_at_1000
-      value: 51.121166666666674
-    - type: ndcg_at_3
-      value: 38.73575
-    - type: ndcg_at_5
-      value: 41.18158333333333
-    - type: precision_at_1
-      value: 33.39966666666667
-    - type: precision_at_10
-      value: 7.738916666666667
-    - type: precision_at_100
-      value: 1.2265833333333331
-    - type: precision_at_1000
-      value: 0.15983333333333336
-    - type: precision_at_3
-      value: 17.967416666666665
-    - type: precision_at_5
-      value: 12.78675
-    - type: recall_at_1
-      value: 28.099166666666665
-    - type: recall_at_10
-      value: 56.27049999999999
-    - type: recall_at_100
-      value: 78.93291666666667
-    - type: recall_at_1000
-      value: 92.81608333333334
-    - type: recall_at_3
-      value: 42.09775
-    - type: recall_at_5
-      value: 48.42533333333334
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackStatsRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 23.663
-    - type: map_at_10
-      value: 30.377
-    - type: map_at_100
-      value: 31.426
-    - type: map_at_1000
-      value: 31.519000000000002
-    - type: map_at_3
-      value: 28.069
-    - type: map_at_5
-      value: 29.256999999999998
-    - type: mrr_at_1
-      value: 26.687
-    - type: mrr_at_10
-      value: 33.107
-    - type: mrr_at_100
-      value: 34.055
-    - type: mrr_at_1000
-      value: 34.117999999999995
-    - type: mrr_at_3
-      value: 31.058000000000003
-    - type: mrr_at_5
-      value: 32.14
-    - type: ndcg_at_1
-      value: 26.687
-    - type: ndcg_at_10
-      value: 34.615
-    - type: ndcg_at_100
-      value: 39.776
-    - type: ndcg_at_1000
-      value: 42.05
-    - type: ndcg_at_3
-      value: 30.322
-    - type: ndcg_at_5
-      value: 32.157000000000004
-    - type: precision_at_1
-      value: 26.687
-    - type: precision_at_10
-      value: 5.491
-    - type: precision_at_100
-      value: 0.877
-    - type: precision_at_1000
-      value: 0.11499999999999999
-    - type: precision_at_3
-      value: 13.139000000000001
-    - type: precision_at_5
-      value: 9.049
-    - type: recall_at_1
-      value: 23.663
-    - type: recall_at_10
-      value: 45.035
-    - type: recall_at_100
-      value: 68.554
-    - type: recall_at_1000
-      value: 85.077
-    - type: recall_at_3
-      value: 32.982
-    - type: recall_at_5
-      value: 37.688
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackTexRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 17.403
-    - type: map_at_10
-      value: 25.197000000000003
-    - type: map_at_100
-      value: 26.355
-    - type: map_at_1000
-      value: 26.487
-    - type: map_at_3
-      value: 22.733
-    - type: map_at_5
-      value: 24.114
-    - type: mrr_at_1
-      value: 21.37
-    - type: mrr_at_10
-      value: 29.091
-    - type: mrr_at_100
-      value: 30.018
-    - type: mrr_at_1000
-      value: 30.096
-    - type: mrr_at_3
-      value: 26.887
-    - type: mrr_at_5
-      value: 28.157
-    - type: ndcg_at_1
-      value: 21.37
-    - type: ndcg_at_10
-      value: 30.026000000000003
-    - type: ndcg_at_100
-      value: 35.416
-    - type: ndcg_at_1000
-      value: 38.45
-    - type: ndcg_at_3
-      value: 25.764
-    - type: ndcg_at_5
-      value: 27.742
-    - type: precision_at_1
-      value: 21.37
-    - type: precision_at_10
-      value: 5.609
-    - type: precision_at_100
-      value: 0.9860000000000001
-    - type: precision_at_1000
-      value: 0.14300000000000002
-    - type: precision_at_3
-      value: 12.423
-    - type: precision_at_5
-      value: 9.009
-    - type: recall_at_1
-      value: 17.403
-    - type: recall_at_10
-      value: 40.573
-    - type: recall_at_100
-      value: 64.818
-    - type: recall_at_1000
-      value: 86.53699999999999
-    - type: recall_at_3
-      value: 28.493000000000002
-    - type: recall_at_5
-      value: 33.660000000000004
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackUnixRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 28.639
-    - type: map_at_10
-      value: 38.951
-    - type: map_at_100
-      value: 40.238
-    - type: map_at_1000
-      value: 40.327
-    - type: map_at_3
-      value: 35.842
-    - type: map_at_5
-      value: 37.617
-    - type: mrr_at_1
-      value: 33.769
-    - type: mrr_at_10
-      value: 43.088
-    - type: mrr_at_100
-      value: 44.03
-    - type: mrr_at_1000
-      value: 44.072
-    - type: mrr_at_3
-      value: 40.656
-    - type: mrr_at_5
-      value: 42.138999999999996
-    - type: ndcg_at_1
-      value: 33.769
-    - type: ndcg_at_10
-      value: 44.676
-    - type: ndcg_at_100
-      value: 50.416000000000004
-    - type: ndcg_at_1000
-      value: 52.227999999999994
-    - type: ndcg_at_3
-      value: 39.494
-    - type: ndcg_at_5
-      value: 42.013
-    - type: precision_at_1
-      value: 33.769
-    - type: precision_at_10
-      value: 7.668
-    - type: precision_at_100
-      value: 1.18
-    - type: precision_at_1000
-      value: 0.145
-    - type: precision_at_3
-      value: 18.221
-    - type: precision_at_5
-      value: 12.966
-    - type: recall_at_1
-      value: 28.639
-    - type: recall_at_10
-      value: 57.687999999999995
-    - type: recall_at_100
-      value: 82.541
-    - type: recall_at_1000
-      value: 94.896
-    - type: recall_at_3
-      value: 43.651
-    - type: recall_at_5
-      value: 49.925999999999995
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackWebmastersRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 29.57
-    - type: map_at_10
-      value: 40.004
-    - type: map_at_100
-      value: 41.75
-    - type: map_at_1000
-      value: 41.97
-    - type: map_at_3
-      value: 36.788
-    - type: map_at_5
-      value: 38.671
-    - type: mrr_at_1
-      value: 35.375
-    - type: mrr_at_10
-      value: 45.121
-    - type: mrr_at_100
-      value: 45.994
-    - type: mrr_at_1000
-      value: 46.04
-    - type: mrr_at_3
-      value: 42.227
-    - type: mrr_at_5
-      value: 43.995
-    - type: ndcg_at_1
-      value: 35.375
-    - type: ndcg_at_10
-      value: 46.392
-    - type: ndcg_at_100
-      value: 52.196
-    - type: ndcg_at_1000
-      value: 54.274
-    - type: ndcg_at_3
-      value: 41.163
-    - type: ndcg_at_5
-      value: 43.813
-    - type: precision_at_1
-      value: 35.375
-    - type: precision_at_10
-      value: 8.676
-    - type: precision_at_100
-      value: 1.678
-    - type: precision_at_1000
-      value: 0.253
-    - type: precision_at_3
-      value: 19.104
-    - type: precision_at_5
-      value: 13.913
-    - type: recall_at_1
-      value: 29.57
-    - type: recall_at_10
-      value: 58.779
-    - type: recall_at_100
-      value: 83.337
-    - type: recall_at_1000
-      value: 95.979
-    - type: recall_at_3
-      value: 44.005
-    - type: recall_at_5
-      value: 50.975
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackWordpressRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 20.832
-    - type: map_at_10
-      value: 29.733999999999998
-    - type: map_at_100
-      value: 30.727
-    - type: map_at_1000
-      value: 30.843999999999998
-    - type: map_at_3
-      value: 26.834999999999997
-    - type: map_at_5
-      value: 28.555999999999997
-    - type: mrr_at_1
-      value: 22.921
-    - type: mrr_at_10
-      value: 31.791999999999998
-    - type: mrr_at_100
-      value: 32.666000000000004
-    - type: mrr_at_1000
-      value: 32.751999999999995
-    - type: mrr_at_3
-      value: 29.144
-    - type: mrr_at_5
-      value: 30.622
-    - type: ndcg_at_1
-      value: 22.921
-    - type: ndcg_at_10
-      value: 34.915
-    - type: ndcg_at_100
-      value: 39.744
-    - type: ndcg_at_1000
-      value: 42.407000000000004
-    - type: ndcg_at_3
-      value: 29.421000000000003
-    - type: ndcg_at_5
-      value: 32.211
-    - type: precision_at_1
-      value: 22.921
-    - type: precision_at_10
-      value: 5.675
-    - type: precision_at_100
-      value: 0.872
-    - type: precision_at_1000
-      value: 0.121
-    - type: precision_at_3
-      value: 12.753999999999998
-    - type: precision_at_5
-      value: 9.353
-    - type: recall_at_1
-      value: 20.832
-    - type: recall_at_10
-      value: 48.795
-    - type: recall_at_100
-      value: 70.703
-    - type: recall_at_1000
-      value: 90.187
-    - type: recall_at_3
-      value: 34.455000000000005
-    - type: recall_at_5
-      value: 40.967
-  - task:
-      type: Retrieval
-    dataset:
-      type: climate-fever
-      name: MTEB ClimateFEVER
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 10.334
-    - type: map_at_10
-      value: 19.009999999999998
-    - type: map_at_100
-      value: 21.129
-    - type: map_at_1000
-      value: 21.328
-    - type: map_at_3
-      value: 15.152
-    - type: map_at_5
-      value: 17.084
-    - type: mrr_at_1
-      value: 23.453
-    - type: mrr_at_10
-      value: 36.099
-    - type: mrr_at_100
-      value: 37.069
-    - type: mrr_at_1000
-      value: 37.104
-    - type: mrr_at_3
-      value: 32.096000000000004
-    - type: mrr_at_5
-      value: 34.451
-    - type: ndcg_at_1
-      value: 23.453
-    - type: ndcg_at_10
-      value: 27.739000000000004
-    - type: ndcg_at_100
-      value: 35.836
-    - type: ndcg_at_1000
-      value: 39.242
-    - type: ndcg_at_3
-      value: 21.263
-    - type: ndcg_at_5
-      value: 23.677
-    - type: precision_at_1
-      value: 23.453
-    - type: precision_at_10
-      value: 9.199
-    - type: precision_at_100
-      value: 1.791
-    - type: precision_at_1000
-      value: 0.242
-    - type: precision_at_3
-      value: 16.2
-    - type: precision_at_5
-      value: 13.147
-    - type: recall_at_1
-      value: 10.334
-    - type: recall_at_10
-      value: 35.177
-    - type: recall_at_100
-      value: 63.009
-    - type: recall_at_1000
-      value: 81.938
-    - type: recall_at_3
-      value: 19.914
-    - type: recall_at_5
-      value: 26.077
-  - task:
-      type: Retrieval
-    dataset:
-      type: dbpedia-entity
-      name: MTEB DBPedia
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 8.212
-    - type: map_at_10
-      value: 17.386
-    - type: map_at_100
-      value: 24.234
-    - type: map_at_1000
-      value: 25.724999999999998
-    - type: map_at_3
-      value: 12.727
-    - type: map_at_5
-      value: 14.785
-    - type: mrr_at_1
-      value: 59.25
-    - type: mrr_at_10
-      value: 68.687
-    - type: mrr_at_100
-      value: 69.133
-    - type: mrr_at_1000
-      value: 69.14099999999999
-    - type: mrr_at_3
-      value: 66.917
-    - type: mrr_at_5
-      value: 67.742
-    - type: ndcg_at_1
-      value: 48.625
-    - type: ndcg_at_10
-      value: 36.675999999999995
-    - type: ndcg_at_100
-      value: 41.543
-    - type: ndcg_at_1000
-      value: 49.241
-    - type: ndcg_at_3
-      value: 41.373
-    - type: ndcg_at_5
-      value: 38.707
-    - type: precision_at_1
-      value: 59.25
-    - type: precision_at_10
-      value: 28.525
-    - type: precision_at_100
-      value: 9.027000000000001
-    - type: precision_at_1000
-      value: 1.8339999999999999
-    - type: precision_at_3
-      value: 44.833
-    - type: precision_at_5
-      value: 37.35
-    - type: recall_at_1
-      value: 8.212
-    - type: recall_at_10
-      value: 23.188
-    - type: recall_at_100
-      value: 48.613
-    - type: recall_at_1000
-      value: 73.093
-    - type: recall_at_3
-      value: 14.419
-    - type: recall_at_5
-      value: 17.798
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/emotion
-      name: MTEB EmotionClassification
-      config: default
-      split: test
-      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
-    metrics:
-    - type: accuracy
-      value: 52.725
-    - type: f1
-      value: 46.50743309855908
-  - task:
-      type: Retrieval
-    dataset:
-      type: fever
-      name: MTEB FEVER
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 55.086
-    - type: map_at_10
-      value: 66.914
-    - type: map_at_100
-      value: 67.321
-    - type: map_at_1000
-      value: 67.341
-    - type: map_at_3
-      value: 64.75800000000001
-    - type: map_at_5
-      value: 66.189
-    - type: mrr_at_1
-      value: 59.28600000000001
-    - type: mrr_at_10
-      value: 71.005
-    - type: mrr_at_100
-      value: 71.304
-    - type: mrr_at_1000
-      value: 71.313
-    - type: mrr_at_3
-      value: 69.037
-    - type: mrr_at_5
-      value: 70.35
-    - type: ndcg_at_1
-      value: 59.28600000000001
-    - type: ndcg_at_10
-      value: 72.695
-    - type: ndcg_at_100
-      value: 74.432
-    - type: ndcg_at_1000
-      value: 74.868
-    - type: ndcg_at_3
-      value: 68.72200000000001
-    - type: ndcg_at_5
-      value: 71.081
-    - type: precision_at_1
-      value: 59.28600000000001
-    - type: precision_at_10
-      value: 9.499
-    - type: precision_at_100
-      value: 1.052
-    - type: precision_at_1000
-      value: 0.11100000000000002
-    - type: precision_at_3
-      value: 27.503
-    - type: precision_at_5
-      value: 17.854999999999997
-    - type: recall_at_1
-      value: 55.086
-    - type: recall_at_10
-      value: 86.453
-    - type: recall_at_100
-      value: 94.028
-    - type: recall_at_1000
-      value: 97.052
-    - type: recall_at_3
-      value: 75.821
-    - type: recall_at_5
-      value: 81.6
-  - task:
-      type: Retrieval
-    dataset:
-      type: fiqa
-      name: MTEB FiQA2018
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 22.262999999999998
-    - type: map_at_10
-      value: 37.488
-    - type: map_at_100
-      value: 39.498
-    - type: map_at_1000
-      value: 39.687
-    - type: map_at_3
-      value: 32.529
-    - type: map_at_5
-      value: 35.455
-    - type: mrr_at_1
-      value: 44.907000000000004
-    - type: mrr_at_10
-      value: 53.239000000000004
-    - type: mrr_at_100
-      value: 54.086
-    - type: mrr_at_1000
-      value: 54.122
-    - type: mrr_at_3
-      value: 51.235
-    - type: mrr_at_5
-      value: 52.415
-    - type: ndcg_at_1
-      value: 44.907000000000004
-    - type: ndcg_at_10
-      value: 45.446
-    - type: ndcg_at_100
-      value: 52.429
-    - type: ndcg_at_1000
-      value: 55.169000000000004
-    - type: ndcg_at_3
-      value: 41.882000000000005
-    - type: ndcg_at_5
-      value: 43.178
-    - type: precision_at_1
-      value: 44.907000000000004
-    - type: precision_at_10
-      value: 12.931999999999999
-    - type: precision_at_100
-      value: 2.025
-    - type: precision_at_1000
-      value: 0.248
-    - type: precision_at_3
-      value: 28.652
-    - type: precision_at_5
-      value: 21.204
-    - type: recall_at_1
-      value: 22.262999999999998
-    - type: recall_at_10
-      value: 52.447
-    - type: recall_at_100
-      value: 78.045
-    - type: recall_at_1000
-      value: 94.419
-    - type: recall_at_3
-      value: 38.064
-    - type: recall_at_5
-      value: 44.769
-  - task:
-      type: Retrieval
-    dataset:
-      type: hotpotqa
-      name: MTEB HotpotQA
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 32.519
-    - type: map_at_10
-      value: 45.831
-    - type: map_at_100
-      value: 46.815
-    - type: map_at_1000
-      value: 46.899
-    - type: map_at_3
-      value: 42.836
-    - type: map_at_5
-      value: 44.65
-    - type: mrr_at_1
-      value: 65.037
-    - type: mrr_at_10
-      value: 72.16
-    - type: mrr_at_100
-      value: 72.51100000000001
-    - type: mrr_at_1000
-      value: 72.53
-    - type: mrr_at_3
-      value: 70.682
-    - type: mrr_at_5
-      value: 71.54599999999999
-    - type: ndcg_at_1
-      value: 65.037
-    - type: ndcg_at_10
-      value: 55.17999999999999
-    - type: ndcg_at_100
-      value: 58.888
-    - type: ndcg_at_1000
-      value: 60.648
-    - type: ndcg_at_3
-      value: 50.501
-    - type: ndcg_at_5
-      value: 52.977
-    - type: precision_at_1
-      value: 65.037
-    - type: precision_at_10
-      value: 11.530999999999999
-    - type: precision_at_100
-      value: 1.4460000000000002
-    - type: precision_at_1000
-      value: 0.168
-    - type: precision_at_3
-      value: 31.483
-    - type: precision_at_5
-      value: 20.845
-    - type: recall_at_1
-      value: 32.519
-    - type: recall_at_10
-      value: 57.657000000000004
-    - type: recall_at_100
-      value: 72.30199999999999
-    - type: recall_at_1000
-      value: 84.024
-    - type: recall_at_3
-      value: 47.225
-    - type: recall_at_5
-      value: 52.113
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/imdb
-      name: MTEB ImdbClassification
-      config: default
-      split: test
-      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
-    metrics:
-    - type: accuracy
-      value: 88.3168
-    - type: ap
-      value: 83.80165516037135
-    - type: f1
-      value: 88.29942471066407
-  - task:
-      type: Retrieval
-    dataset:
-      type: msmarco
-      name: MTEB MSMARCO
-      config: default
-      split: dev
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 20.724999999999998
-    - type: map_at_10
-      value: 32.736
-    - type: map_at_100
-      value: 33.938
-    - type: map_at_1000
-      value: 33.991
-    - type: map_at_3
-      value: 28.788000000000004
-    - type: map_at_5
-      value: 31.016
-    - type: mrr_at_1
-      value: 21.361
-    - type: mrr_at_10
-      value: 33.323
-    - type: mrr_at_100
-      value: 34.471000000000004
-    - type: mrr_at_1000
-      value: 34.518
-    - type: mrr_at_3
-      value: 29.453000000000003
-    - type: mrr_at_5
-      value: 31.629
-    - type: ndcg_at_1
-      value: 21.361
-    - type: ndcg_at_10
-      value: 39.649
-    - type: ndcg_at_100
-      value: 45.481
-    - type: ndcg_at_1000
-      value: 46.775
-    - type: ndcg_at_3
-      value: 31.594
-    - type: ndcg_at_5
-      value: 35.543
-    - type: precision_at_1
-      value: 21.361
-    - type: precision_at_10
-      value: 6.3740000000000006
-    - type: precision_at_100
-      value: 0.931
-    - type: precision_at_1000
-      value: 0.104
-    - type: precision_at_3
-      value: 13.514999999999999
-    - type: precision_at_5
-      value: 10.100000000000001
-    - type: recall_at_1
-      value: 20.724999999999998
-    - type: recall_at_10
-      value: 61.034
-    - type: recall_at_100
-      value: 88.062
-    - type: recall_at_1000
-      value: 97.86399999999999
-    - type: recall_at_3
-      value: 39.072
-    - type: recall_at_5
-      value: 48.53
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/mtop_domain
-      name: MTEB MTOPDomainClassification (en)
-      config: en
-      split: test
-      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
-    metrics:
-    - type: accuracy
-      value: 93.8919288645691
-    - type: f1
-      value: 93.57059586398059
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/mtop_intent
-      name: MTEB MTOPIntentClassification (en)
-      config: en
-      split: test
-      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
-    metrics:
-    - type: accuracy
-      value: 67.97993616051072
-    - type: f1
-      value: 48.244319183606535
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_massive_intent
-      name: MTEB MassiveIntentClassification (en)
-      config: en
-      split: test
-      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
-    metrics:
-    - type: accuracy
-      value: 68.90047074646941
-    - type: f1
-      value: 66.48999056063725
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_massive_scenario
-      name: MTEB MassiveScenarioClassification (en)
-      config: en
-      split: test
-      revision: 7d571f92784cd94a019292a1f45445077d0ef634
-    metrics:
-    - type: accuracy
-      value: 73.34566240753195
-    - type: f1
-      value: 73.54164154290658
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/medrxiv-clustering-p2p
-      name: MTEB MedrxivClusteringP2P
-      config: default
-      split: test
-      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
-    metrics:
-    - type: v_measure
-      value: 34.21866934757011
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/medrxiv-clustering-s2s
-      name: MTEB MedrxivClusteringS2S
-      config: default
-      split: test
-      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
-    metrics:
-    - type: v_measure
-      value: 32.000936217235534
-  - task:
-      type: Reranking
-    dataset:
-      type: mteb/mind_small
-      name: MTEB MindSmallReranking
-      config: default
-      split: test
-      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
-    metrics:
-    - type: map
-      value: 31.68189362520352
-    - type: mrr
-      value: 32.69603637784303
-  - task:
-      type: Retrieval
-    dataset:
-      type: nfcorpus
-      name: MTEB NFCorpus
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 6.078
-    - type: map_at_10
-      value: 12.671
-    - type: map_at_100
-      value: 16.291
-    - type: map_at_1000
-      value: 17.855999999999998
-    - type: map_at_3
-      value: 9.610000000000001
-    - type: map_at_5
-      value: 11.152
-    - type: mrr_at_1
-      value: 43.963
-    - type: mrr_at_10
-      value: 53.173
-    - type: mrr_at_100
-      value: 53.718999999999994
-    - type: mrr_at_1000
-      value: 53.756
-    - type: mrr_at_3
-      value: 50.980000000000004
-    - type: mrr_at_5
-      value: 52.42
-    - type: ndcg_at_1
-      value: 42.415000000000006
-    - type: ndcg_at_10
-      value: 34.086
-    - type: ndcg_at_100
-      value: 32.545
-    - type: ndcg_at_1000
-      value: 41.144999999999996
-    - type: ndcg_at_3
-      value: 39.434999999999995
-    - type: ndcg_at_5
-      value: 37.888
-    - type: precision_at_1
-      value: 43.653
-    - type: precision_at_10
-      value: 25.014999999999997
-    - type: precision_at_100
-      value: 8.594
-    - type: precision_at_1000
-      value: 2.169
-    - type: precision_at_3
-      value: 37.049
-    - type: precision_at_5
-      value: 33.065
-    - type: recall_at_1
-      value: 6.078
-    - type: recall_at_10
-      value: 16.17
-    - type: recall_at_100
-      value: 34.512
-    - type: recall_at_1000
-      value: 65.447
-    - type: recall_at_3
-      value: 10.706
-    - type: recall_at_5
-      value: 13.158
-  - task:
-      type: Retrieval
-    dataset:
-      type: nq
-      name: MTEB NQ
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 27.378000000000004
-    - type: map_at_10
-      value: 42.178
-    - type: map_at_100
-      value: 43.32
-    - type: map_at_1000
-      value: 43.358000000000004
-    - type: map_at_3
-      value: 37.474000000000004
-    - type: map_at_5
-      value: 40.333000000000006
-    - type: mrr_at_1
-      value: 30.823
-    - type: mrr_at_10
-      value: 44.626
-    - type: mrr_at_100
-      value: 45.494
-    - type: mrr_at_1000
-      value: 45.519
-    - type: mrr_at_3
-      value: 40.585
-    - type: mrr_at_5
-      value: 43.146
-    - type: ndcg_at_1
-      value: 30.794
-    - type: ndcg_at_10
-      value: 50.099000000000004
-    - type: ndcg_at_100
-      value: 54.900999999999996
-    - type: ndcg_at_1000
-      value: 55.69499999999999
-    - type: ndcg_at_3
-      value: 41.238
-    - type: ndcg_at_5
-      value: 46.081
-    - type: precision_at_1
-      value: 30.794
-    - type: precision_at_10
-      value: 8.549
-    - type: precision_at_100
-      value: 1.124
-    - type: precision_at_1000
-      value: 0.12
-    - type: precision_at_3
-      value: 18.926000000000002
-    - type: precision_at_5
-      value: 14.16
-    - type: recall_at_1
-      value: 27.378000000000004
-    - type: recall_at_10
-      value: 71.842
-    - type: recall_at_100
-      value: 92.565
-    - type: recall_at_1000
-      value: 98.402
-    - type: recall_at_3
-      value: 49.053999999999995
-    - type: recall_at_5
-      value: 60.207
-  - task:
-      type: Retrieval
-    dataset:
-      type: quora
-      name: MTEB QuoraRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 70.557
-    - type: map_at_10
-      value: 84.729
-    - type: map_at_100
-      value: 85.369
-    - type: map_at_1000
-      value: 85.382
-    - type: map_at_3
-      value: 81.72
-    - type: map_at_5
-      value: 83.613
-    - type: mrr_at_1
-      value: 81.3
-    - type: mrr_at_10
-      value: 87.488
-    - type: mrr_at_100
-      value: 87.588
-    - type: mrr_at_1000
-      value: 87.589
-    - type: mrr_at_3
-      value: 86.53
-    - type: mrr_at_5
-      value: 87.18599999999999
-    - type: ndcg_at_1
-      value: 81.28999999999999
-    - type: ndcg_at_10
-      value: 88.442
-    - type: ndcg_at_100
-      value: 89.637
-    - type: ndcg_at_1000
-      value: 89.70700000000001
-    - type: ndcg_at_3
-      value: 85.55199999999999
-    - type: ndcg_at_5
-      value: 87.154
-    - type: precision_at_1
-      value: 81.28999999999999
-    - type: precision_at_10
-      value: 13.489999999999998
-    - type: precision_at_100
-      value: 1.54
-    - type: precision_at_1000
-      value: 0.157
-    - type: precision_at_3
-      value: 37.553
-    - type: precision_at_5
-      value: 24.708
-    - type: recall_at_1
-      value: 70.557
-    - type: recall_at_10
-      value: 95.645
-    - type: recall_at_100
-      value: 99.693
-    - type: recall_at_1000
-      value: 99.995
-    - type: recall_at_3
-      value: 87.359
-    - type: recall_at_5
-      value: 91.89699999999999
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/reddit-clustering
-      name: MTEB RedditClustering
-      config: default
-      split: test
-      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
-    metrics:
-    - type: v_measure
-      value: 63.65060114776209
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/reddit-clustering-p2p
-      name: MTEB RedditClusteringP2P
-      config: default
-      split: test
-      revision: 282350215ef01743dc01b456c7f5241fa8937f16
-    metrics:
-    - type: v_measure
-      value: 64.63271250680617
-  - task:
-      type: Retrieval
-    dataset:
-      type: scidocs
-      name: MTEB SCIDOCS
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 4.263
-    - type: map_at_10
-      value: 10.801
-    - type: map_at_100
-      value: 12.888
-    - type: map_at_1000
-      value: 13.224
-    - type: map_at_3
-      value: 7.362
-    - type: map_at_5
-      value: 9.149000000000001
-    - type: mrr_at_1
-      value: 21
-    - type: mrr_at_10
-      value: 31.416
-    - type: mrr_at_100
-      value: 32.513
-    - type: mrr_at_1000
-      value: 32.58
-    - type: mrr_at_3
-      value: 28.116999999999997
-    - type: mrr_at_5
-      value: 29.976999999999997
-    - type: ndcg_at_1
-      value: 21
-    - type: ndcg_at_10
-      value: 18.551000000000002
-    - type: ndcg_at_100
-      value: 26.657999999999998
-    - type: ndcg_at_1000
-      value: 32.485
-    - type: ndcg_at_3
-      value: 16.834
-    - type: ndcg_at_5
-      value: 15.204999999999998
-    - type: precision_at_1
-      value: 21
-    - type: precision_at_10
-      value: 9.84
-    - type: precision_at_100
-      value: 2.16
-    - type: precision_at_1000
-      value: 0.35500000000000004
-    - type: precision_at_3
-      value: 15.667
-    - type: precision_at_5
-      value: 13.62
-    - type: recall_at_1
-      value: 4.263
-    - type: recall_at_10
-      value: 19.922
-    - type: recall_at_100
-      value: 43.808
-    - type: recall_at_1000
-      value: 72.14500000000001
-    - type: recall_at_3
-      value: 9.493
-    - type: recall_at_5
-      value: 13.767999999999999
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sickr-sts
-      name: MTEB SICK-R
-      config: default
-      split: test
-      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
-    metrics:
-    - type: cos_sim_spearman
-      value: 81.27446313317233
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts12-sts
-      name: MTEB STS12
-      config: default
-      split: test
-      revision: a0d554a64d88156834ff5ae9920b964011b16384
-    metrics:
-    - type: cos_sim_spearman
-      value: 76.27963301217527
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts13-sts
-      name: MTEB STS13
-      config: default
-      split: test
-      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
-    metrics:
-    - type: cos_sim_spearman
-      value: 88.18495048450949
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts14-sts
-      name: MTEB STS14
-      config: default
-      split: test
-      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
-    metrics:
-    - type: cos_sim_spearman
-      value: 81.91982338692046
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts15-sts
-      name: MTEB STS15
-      config: default
-      split: test
-      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
-    metrics:
-    - type: cos_sim_spearman
-      value: 89.00896818385291
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts16-sts
-      name: MTEB STS16
-      config: default
-      split: test
-      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
-    metrics:
-    - type: cos_sim_spearman
-      value: 85.48814644586132
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts17-crosslingual-sts
-      name: MTEB STS17 (en-en)
-      config: en-en
-      split: test
-      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
-    metrics:
-    - type: cos_sim_spearman
-      value: 90.30116926966582
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts22-crosslingual-sts
-      name: MTEB STS22 (en)
-      config: en
-      split: test
-      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
-    metrics:
-    - type: cos_sim_spearman
-      value: 67.74132963032342
-  - task:
-      type: STS
-    dataset:
-      type: mteb/stsbenchmark-sts
-      name: MTEB STSBenchmark
-      config: default
-      split: test
-      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
-    metrics:
-    - type: cos_sim_spearman
-      value: 86.87741355780479
-  - task:
-      type: Reranking
-    dataset:
-      type: mteb/scidocs-reranking
-      name: MTEB SciDocsRR
-      config: default
-      split: test
-      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
-    metrics:
-    - type: map
-      value: 82.0019012295875
-    - type: mrr
-      value: 94.70267024188593
-  - task:
-      type: Retrieval
-    dataset:
-      type: scifact
-      name: MTEB SciFact
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 50.05
-    - type: map_at_10
-      value: 59.36
-    - type: map_at_100
-      value: 59.967999999999996
-    - type: map_at_1000
-      value: 60.023
-    - type: map_at_3
-      value: 56.515
-    - type: map_at_5
-      value: 58.272999999999996
-    - type: mrr_at_1
-      value: 53
-    - type: mrr_at_10
-      value: 61.102000000000004
-    - type: mrr_at_100
-      value: 61.476
-    - type: mrr_at_1000
-      value: 61.523
-    - type: mrr_at_3
-      value: 58.778
-    - type: mrr_at_5
-      value: 60.128
-    - type: ndcg_at_1
-      value: 53
-    - type: ndcg_at_10
-      value: 64.43100000000001
-    - type: ndcg_at_100
-      value: 66.73599999999999
-    - type: ndcg_at_1000
-      value: 68.027
-    - type: ndcg_at_3
-      value: 59.279
-    - type: ndcg_at_5
-      value: 61.888
-    - type: precision_at_1
-      value: 53
-    - type: precision_at_10
-      value: 8.767
-    - type: precision_at_100
-      value: 1.01
-    - type: precision_at_1000
-      value: 0.11100000000000002
-    - type: precision_at_3
-      value: 23.444000000000003
-    - type: precision_at_5
-      value: 15.667
-    - type: recall_at_1
-      value: 50.05
-    - type: recall_at_10
-      value: 78.511
-    - type: recall_at_100
-      value: 88.5
-    - type: recall_at_1000
-      value: 98.333
-    - type: recall_at_3
-      value: 64.117
-    - type: recall_at_5
-      value: 70.867
-  - task:
-      type: PairClassification
-    dataset:
-      type: mteb/sprintduplicatequestions-pairclassification
-      name: MTEB SprintDuplicateQuestions
-      config: default
-      split: test
-      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
-    metrics:
-    - type: cos_sim_accuracy
-      value: 99.72178217821782
-    - type: cos_sim_ap
-      value: 93.0728601593541
-    - type: cos_sim_f1
-      value: 85.6727976766699
-    - type: cos_sim_precision
-      value: 83.02063789868667
-    - type: cos_sim_recall
-      value: 88.5
-    - type: dot_accuracy
-      value: 99.72178217821782
-    - type: dot_ap
-      value: 93.07287396168348
-    - type: dot_f1
-      value: 85.6727976766699
-    - type: dot_precision
-      value: 83.02063789868667
-    - type: dot_recall
-      value: 88.5
-    - type: euclidean_accuracy
-      value: 99.72178217821782
-    - type: euclidean_ap
-      value: 93.07285657982895
-    - type: euclidean_f1
-      value: 85.6727976766699
-    - type: euclidean_precision
-      value: 83.02063789868667
-    - type: euclidean_recall
-      value: 88.5
-    - type: manhattan_accuracy
-      value: 99.72475247524753
-    - type: manhattan_ap
-      value: 93.02792973059809
-    - type: manhattan_f1
-      value: 85.7727737973388
-    - type: manhattan_precision
-      value: 87.84067085953879
-    - type: manhattan_recall
-      value: 83.8
-    - type: max_accuracy
-      value: 99.72475247524753
-    - type: max_ap
-      value: 93.07287396168348
-    - type: max_f1
-      value: 85.7727737973388
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/stackexchange-clustering
-      name: MTEB StackExchangeClustering
-      config: default
-      split: test
-      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
-    metrics:
-    - type: v_measure
-      value: 68.77583615550819
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/stackexchange-clustering-p2p
-      name: MTEB StackExchangeClusteringP2P
-      config: default
-      split: test
-      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
-    metrics:
-    - type: v_measure
-      value: 36.151636938606956
-  - task:
-      type: Reranking
-    dataset:
-      type: mteb/stackoverflowdupquestions-reranking
-      name: MTEB StackOverflowDupQuestions
-      config: default
-      split: test
-      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
-    metrics:
-    - type: map
-      value: 52.16607939471187
-    - type: mrr
-      value: 52.95172046091163
-  - task:
-      type: Summarization
-    dataset:
-      type: mteb/summeval
-      name: MTEB SummEval
-      config: default
-      split: test
-      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
-    metrics:
-    - type: cos_sim_pearson
-      value: 31.314646669495666
-    - type: cos_sim_spearman
-      value: 31.83562491439455
-    - type: dot_pearson
-      value: 31.314590842874157
-    - type: dot_spearman
-      value: 31.83363065810437
-  - task:
-      type: Retrieval
-    dataset:
-      type: trec-covid
-      name: MTEB TRECCOVID
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 0.198
-    - type: map_at_10
-      value: 1.3010000000000002
-    - type: map_at_100
-      value: 7.2139999999999995
-    - type: map_at_1000
-      value: 20.179
-    - type: map_at_3
-      value: 0.528
-    - type: map_at_5
-      value: 0.8019999999999999
-    - type: mrr_at_1
-      value: 72
-    - type: mrr_at_10
-      value: 83.39999999999999
-    - type: mrr_at_100
-      value: 83.39999999999999
-    - type: mrr_at_1000
-      value: 83.39999999999999
-    - type: mrr_at_3
-      value: 81.667
-    - type: mrr_at_5
-      value: 83.06700000000001
-    - type: ndcg_at_1
-      value: 66
-    - type: ndcg_at_10
-      value: 58.059000000000005
-    - type: ndcg_at_100
-      value: 44.316
-    - type: ndcg_at_1000
-      value: 43.147000000000006
-    - type: ndcg_at_3
-      value: 63.815999999999995
-    - type: ndcg_at_5
-      value: 63.005
-    - type: precision_at_1
-      value: 72
-    - type: precision_at_10
-      value: 61.4
-    - type: precision_at_100
-      value: 45.62
-    - type: precision_at_1000
-      value: 19.866
-    - type: precision_at_3
-      value: 70
-    - type: precision_at_5
-      value: 68.8
-    - type: recall_at_1
-      value: 0.198
-    - type: recall_at_10
-      value: 1.517
-    - type: recall_at_100
-      value: 10.587
-    - type: recall_at_1000
-      value: 41.233
-    - type: recall_at_3
-      value: 0.573
-    - type: recall_at_5
-      value: 0.907
-  - task:
-      type: Retrieval
-    dataset:
-      type: webis-touche2020
-      name: MTEB Touche2020
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 1.894
-    - type: map_at_10
-      value: 8.488999999999999
-    - type: map_at_100
-      value: 14.445
-    - type: map_at_1000
-      value: 16.078
-    - type: map_at_3
-      value: 4.589
-    - type: map_at_5
-      value: 6.019
-    - type: mrr_at_1
-      value: 22.448999999999998
-    - type: mrr_at_10
-      value: 39.82
-    - type: mrr_at_100
-      value: 40.752
-    - type: mrr_at_1000
-      value: 40.771
-    - type: mrr_at_3
-      value: 34.354
-    - type: mrr_at_5
-      value: 37.721
-    - type: ndcg_at_1
-      value: 19.387999999999998
-    - type: ndcg_at_10
-      value: 21.563
-    - type: ndcg_at_100
-      value: 33.857
-    - type: ndcg_at_1000
-      value: 46.199
-    - type: ndcg_at_3
-      value: 22.296
-    - type: ndcg_at_5
-      value: 21.770999999999997
-    - type: precision_at_1
-      value: 22.448999999999998
-    - type: precision_at_10
-      value: 19.796
-    - type: precision_at_100
-      value: 7.142999999999999
-    - type: precision_at_1000
-      value: 1.541
-    - type: precision_at_3
-      value: 24.490000000000002
-    - type: precision_at_5
-      value: 22.448999999999998
-    - type: recall_at_1
-      value: 1.894
-    - type: recall_at_10
-      value: 14.931
-    - type: recall_at_100
-      value: 45.524
-    - type: recall_at_1000
-      value: 83.243
-    - type: recall_at_3
-      value: 5.712
-    - type: recall_at_5
-      value: 8.386000000000001
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/toxic_conversations_50k
-      name: MTEB ToxicConversationsClassification
-      config: default
-      split: test
-      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
-    metrics:
-    - type: accuracy
-      value: 71.049
-    - type: ap
-      value: 13.85116971310922
-    - type: f1
-      value: 54.37504302487686
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/tweet_sentiment_extraction
-      name: MTEB TweetSentimentExtractionClassification
-      config: default
-      split: test
-      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
-    metrics:
-    - type: accuracy
-      value: 64.1312959818902
-    - type: f1
-      value: 64.11413877009383
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/twentynewsgroups-clustering
-      name: MTEB TwentyNewsgroupsClustering
-      config: default
-      split: test
-      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
-    metrics:
-    - type: v_measure
-      value: 54.13103431861502
-  - task:
-      type: PairClassification
-    dataset:
-      type: mteb/twittersemeval2015-pairclassification
-      name: MTEB TwitterSemEval2015
-      config: default
-      split: test
-      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
-    metrics:
-    - type: cos_sim_accuracy
-      value: 87.327889372355
-    - type: cos_sim_ap
-      value: 77.42059895975699
-    - type: cos_sim_f1
-      value: 71.02706903250873
-    - type: cos_sim_precision
-      value: 69.75324344950394
-    - type: cos_sim_recall
-      value: 72.34828496042216
-    - type: dot_accuracy
-      value: 87.327889372355
-    - type: dot_ap
-      value: 77.4209479346677
-    - type: dot_f1
-      value: 71.02706903250873
-    - type: dot_precision
-      value: 69.75324344950394
-    - type: dot_recall
-      value: 72.34828496042216
-    - type: euclidean_accuracy
-      value: 87.327889372355
-    - type: euclidean_ap
-      value: 77.42096495861037
-    - type: euclidean_f1
-      value: 71.02706903250873
-    - type: euclidean_precision
-      value: 69.75324344950394
-    - type: euclidean_recall
-      value: 72.34828496042216
-    - type: manhattan_accuracy
-      value: 87.31000774870358
-    - type: manhattan_ap
-      value: 77.38930750711619
-    - type: manhattan_f1
-      value: 71.07935314027831
-    - type: manhattan_precision
-      value: 67.70957726295677
-    - type: manhattan_recall
-      value: 74.80211081794195
-    - type: max_accuracy
-      value: 87.327889372355
-    - type: max_ap
-      value: 77.42096495861037
-    - type: max_f1
-      value: 71.07935314027831
-  - task:
-      type: PairClassification
-    dataset:
-      type: mteb/twitterurlcorpus-pairclassification
-      name: MTEB TwitterURLCorpus
-      config: default
-      split: test
-      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
-    metrics:
-    - type: cos_sim_accuracy
-      value: 89.58939729110878
-    - type: cos_sim_ap
-      value: 87.17594155025475
-    - type: cos_sim_f1
-      value: 79.21146953405018
-    - type: cos_sim_precision
-      value: 76.8918527109307
-    - type: cos_sim_recall
-      value: 81.67539267015707
-    - type: dot_accuracy
-      value: 89.58939729110878
-    - type: dot_ap
-      value: 87.17593963273593
-    - type: dot_f1
-      value: 79.21146953405018
-    - type: dot_precision
-      value: 76.8918527109307
-    - type: dot_recall
-      value: 81.67539267015707
-    - type: euclidean_accuracy
-      value: 89.58939729110878
-    - type: euclidean_ap
-      value: 87.17592466925834
-    - type: euclidean_f1
-      value: 79.21146953405018
-    - type: euclidean_precision
-      value: 76.8918527109307
-    - type: euclidean_recall
-      value: 81.67539267015707
-    - type: manhattan_accuracy
-      value: 89.62626615438352
-    - type: manhattan_ap
-      value: 87.16589873161546
-    - type: manhattan_f1
-      value: 79.25143598295348
-    - type: manhattan_precision
-      value: 76.39494177323712
-    - type: manhattan_recall
-      value: 82.32984293193716
-    - type: max_accuracy
-      value: 89.62626615438352
-    - type: max_ap
-      value: 87.17594155025475
-    - type: max_f1
-      value: 79.25143598295348
 ---
 
-# hkunlp/instructor-large
-We introduce **Instructor**👨‍🏫, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation, etc.) and domains (e.g., science, finance, etc.) ***by simply providing the task instruction, without any finetuning***. Instructor👨‍ achieves sota on 70 diverse embedding tasks ([MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard))!
-The model is easy to use with **our customized** `sentence-transformer` library. For more details, check out [our paper](https://arxiv.org/abs/2212.09741) and [project page](https://instructor-embedding.github.io/)! 
+# **My LLM Model: Dementia Knowledge Assistant**
 
-**************************** **Updates** ****************************
+**Model Name:** `Dementia-llm-model`  
+**Description:**  
+This is a fine-tuned **Large Language Model (LLM)** designed to assist with dementia-related knowledge retrieval and question-answering tasks. The model uses advanced embeddings (`hkunlp/instructor-large`) and a **FAISS vector store** for efficient contextual search and retrieval.
 
-* 12/28: We released a new [checkpoint](https://huggingface.co/hkunlp/instructor-large) trained with hard negatives, which gives better performance.
-* 12/21: We released our [paper](https://arxiv.org/abs/2212.09741), [code](https://github.com/HKUNLP/instructor-embedding), [checkpoint](https://huggingface.co/hkunlp/instructor-large) and [project page](https://instructor-embedding.github.io/)! Check them out!
+---
 
-## Quick start
-<hr />
+## **Model Summary**
 
-## Installation
-```bash
-pip install InstructorEmbedding
-```
+This LLM is fine-tuned on a dataset specifically curated for dementia-related content, including medical knowledge, patient care, and treatment practices. It leverages state-of-the-art embeddings to generate accurate and contextually relevant answers to user queries. The model supports researchers, caregivers, and medical professionals in accessing domain-specific information quickly.
 
-## Compute your customized embeddings
-Then you can use the model like this to calculate domain-specific and task-aware embeddings:
-```python
-from InstructorEmbedding import INSTRUCTOR
-model = INSTRUCTOR('hkunlp/instructor-large')
-sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments"
-instruction = "Represent the Science title:"
-embeddings = model.encode([[instruction,sentence]])
-print(embeddings)
-```
+---
 
-## Use cases
-<hr />
+## **Key Features**
+- **Domain-Specific Knowledge:** Trained on a dementia-related dataset for precise answers.
+- **Embeddings:** Utilizes the `hkunlp/instructor-large` embedding model for semantic understanding.
+- **Retrieval-augmented QA:** Employs FAISS vector databases for efficient document retrieval.
+- **Custom Prompting:** Generates responses based on well-designed prompts to ensure factual accuracy.
 
-## Calculate embeddings for your customized texts
-If you want to calculate customized embeddings for specific sentences, you may follow the unified template to write instructions: 
+---
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Represent the `domain` `text_type` for `task_objective`:
-* `domain` is optional, and it specifies the domain of the text, e.g., science, finance, medicine, etc.
-* `text_type` is required, and it specifies the encoding unit, e.g., sentence, document, paragraph, etc.
-* `task_objective` is optional, and it specifies the objective of embedding, e.g., retrieve a document, classify the sentence, etc.
+## **Intended Use**
+- **Primary Use Case:** Question-answering related to dementia.
+- **Secondary Use Cases:** Exploring dementia knowledge, aiding medical students or caregivers in understanding dementia-related topics, and supporting researchers.
+- **Input Format:** Text queries in natural language.
+- **Output Format:** Natural language responses relevant to the context provided.
 
-## Calculate Sentence similarities
-You can further use the model to compute similarities between two groups of sentences, with **customized embeddings**.
-```python
-from sklearn.metrics.pairwise import cosine_similarity
-sentences_a = [['Represent the Science sentence: ','Parton energy loss in QCD matter'], 
-               ['Represent the Financial statement: ','The Federal Reserve on Wednesday raised its benchmark interest rate.']]
-sentences_b = [['Represent the Science sentence: ','The Chiral Phase Transition in Dissipative Dynamics'],
-               ['Represent the Financial statement: ','The funds rose less than 0.5 per cent on Friday']]
-embeddings_a = model.encode(sentences_a)
-embeddings_b = model.encode(sentences_b)
-similarities = cosine_similarity(embeddings_a,embeddings_b)
-print(similarities)
-```
+---
+
+## **Limitations**
+- **Context Dependency:** Model outputs are only as good as the context provided by the FAISS retriever. If the context is insufficient, the model may respond with "I don't know."
+- **Static Knowledge:** The model is limited to the knowledge present in its training dataset. It may not include the latest medical breakthroughs or research after the training cutoff.
+- **Biases:** The model might inherit biases present in the training data.
+
+---
+
+## **How to Use**
+
+### **Using the Model Programmatically**
+You can use the model directly in Python:
 
-## Information Retrieval
-You can also use **customized embeddings** for information retrieval.
 ```python
-import numpy as np
-from sklearn.metrics.pairwise import cosine_similarity
-query  = [['Represent the Wikipedia question for retrieving supporting documents: ','where is the food stored in a yam plant']]
-corpus = [['Represent the Wikipedia document for retrieval: ','Capitalism has been dominant in the Western world since the end of feudalism, but most feel[who?] that the term "mixed economies" more precisely describes most contemporary economies, due to their containing both private-owned and state-owned enterprises. In capitalism, prices determine the demand-supply scale. For example, higher demand for certain goods and services lead to higher prices and lower demand for certain goods lead to lower prices.'],
-          ['Represent the Wikipedia document for retrieval: ',"The disparate impact theory is especially controversial under the Fair Housing Act because the Act regulates many activities relating to housing, insurance, and mortgage loans—and some scholars have argued that the theory's use under the Fair Housing Act, combined with extensions of the Community Reinvestment Act, contributed to rise of sub-prime lending and the crash of the U.S. housing market and ensuing global economic recession"],
-          ['Represent the Wikipedia document for retrieval: ','Disparate impact in United States labor law refers to practices in employment, housing, and other areas that adversely affect one group of people of a protected characteristic more than another, even though rules applied by employers or landlords are formally neutral. Although the protected classes vary by statute, most federal civil rights laws protect based on race, color, religion, national origin, and sex as protected traits, and some laws include disability status and other traits as well.']]
-query_embeddings = model.encode(query)
-corpus_embeddings = model.encode(corpus)
-similarities = cosine_similarity(query_embeddings,corpus_embeddings)
-retrieved_doc_id = np.argmax(similarities)
-print(retrieved_doc_id)
+from transformers import pipeline
+
+model_name = "rohitashva/my-llm-model"
+
+# Load the model and tokenizer
+qa_pipeline = pipeline("question-answering", model=model_name)
+
+# Example Query
+result = qa_pipeline({
+    "question": "What are the symptoms of early-stage dementia?",
+    "context": "Provide relevant details from a dementia dataset."
+})
+
+print(result)
 ```
 
-## Clustering
-Use **customized embeddings** for clustering texts in groups.
-```python
-import sklearn.cluster
-sentences = [['Represent the Medicine sentence for clustering: ','Dynamical Scalar Degree of Freedom in Horava-Lifshitz Gravity'],
-             ['Represent the Medicine sentence for clustering: ','Comparison of Atmospheric Neutrino Flux Calculations at Low Energies'],
-             ['Represent the Medicine sentence for clustering: ','Fermion Bags in the Massive Gross-Neveu Model'],
-             ['Represent the Medicine sentence for clustering: ',"QCD corrections to Associated t-tbar-H production at the Tevatron"],
-             ['Represent the Medicine sentence for clustering: ','A New Analysis of the R Measurements: Resonance Parameters of the Higher,  Vector States of Charmonium']]
-embeddings = model.encode(sentences)
-clustering_model = sklearn.cluster.MiniBatchKMeans(n_clusters=2)
-clustering_model.fit(embeddings)
-cluster_assignment = clustering_model.labels_
-print(cluster_assignment)
-```
+---
+### **Training Details**
+
+	•	Base Model: hkunlp/instructor-large
+	•	Frameworks: PyTorch, Transformers
+	•	Embedding Model: HuggingFace Embeddings (hkunlp/instructor-large)
+	•	Fine-Tuning: FAISS-based vector retrieval augmented with dementia-specific content.
+	•	Hardware: Trained on a GPU with sufficient VRAM for embeddings and fine-tuning tasks.
+
+---
+
+
+## Further Information
+
+### Dataset
+
+The model was trained on a proprietary dementia-specific dataset, including structured knowledge, medical texts, and patient case studies. The data is preprocessed into embeddings for efficient retrieval.
+
+### Model Performance
+
+	•	Accuracy: Validated on a subset of dementia-related QA pairs.
+	•	Response Time: Optimized for fast retrieval via FAISS vector storage.
+
+### Deployment
+
+	•	Hugging Face Spaces: The model is deployed on Hugging Face Spaces, enabling users to interact via a web-based interface.
+	•	API Support: The model is available for integration into custom workflows using the Hugging Face Inference API.
+
+### Acknowledgments
+
+	•	Hugging Face team for the transformers library.
+	•	Contributors to the hkunlp/instructor-large embedding model.
+	•	Medical experts and datasets used for model fine-tuning.
+