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+ - type: ndcg_at_1
1698
+ value: 60
1699
+ - type: ndcg_at_10
1700
+ value: 71.628
1701
+ - type: ndcg_at_100
1702
+ value: 74.076
1703
+ - type: ndcg_at_1000
1704
+ value: 74.717
1705
+ - type: ndcg_at_3
1706
+ value: 67.51
1707
+ - type: ndcg_at_5
1708
+ value: 69.393
1709
+ - type: precision_at_1
1710
+ value: 60
1711
+ - type: precision_at_10
1712
+ value: 9.433
1713
+ - type: precision_at_100
1714
+ value: 1.0699999999999998
1715
+ - type: precision_at_1000
1716
+ value: 0.11199999999999999
1717
+ - type: precision_at_3
1718
+ value: 26.444000000000003
1719
+ - type: precision_at_5
1720
+ value: 17.2
1721
+ - type: recall_at_1
1722
+ value: 57.05
1723
+ - type: recall_at_10
1724
+ value: 83.289
1725
+ - type: recall_at_100
1726
+ value: 94.267
1727
+ - type: recall_at_1000
1728
+ value: 99.333
1729
+ - type: recall_at_3
1730
+ value: 72.35000000000001
1731
+ - type: recall_at_5
1732
+ value: 77
1733
+ - task:
1734
+ type: Retrieval
1735
+ dataset:
1736
+ type: trec-covid
1737
+ name: MTEB TRECCOVID
1738
+ config: default
1739
+ split: test
1740
+ revision: None
1741
+ metrics:
1742
+ - type: map_at_1
1743
+ value: 0.242
1744
+ - type: map_at_10
1745
+ value: 2.153
1746
+ - type: map_at_100
1747
+ value: 13.045000000000002
1748
+ - type: map_at_1000
1749
+ value: 31.039
1750
+ - type: map_at_3
1751
+ value: 0.709
1752
+ - type: map_at_5
1753
+ value: 1.138
1754
+ - type: mrr_at_1
1755
+ value: 94
1756
+ - type: mrr_at_10
1757
+ value: 95.65
1758
+ - type: mrr_at_100
1759
+ value: 95.65
1760
+ - type: mrr_at_1000
1761
+ value: 95.65
1762
+ - type: mrr_at_3
1763
+ value: 95
1764
+ - type: mrr_at_5
1765
+ value: 95.39999999999999
1766
+ - type: ndcg_at_1
1767
+ value: 89
1768
+ - type: ndcg_at_10
1769
+ value: 83.39999999999999
1770
+ - type: ndcg_at_100
1771
+ value: 64.116
1772
+ - type: ndcg_at_1000
1773
+ value: 56.501000000000005
1774
+ - type: ndcg_at_3
1775
+ value: 88.061
1776
+ - type: ndcg_at_5
1777
+ value: 86.703
1778
+ - type: precision_at_1
1779
+ value: 94
1780
+ - type: precision_at_10
1781
+ value: 87.4
1782
+ - type: precision_at_100
1783
+ value: 65.58
1784
+ - type: precision_at_1000
1785
+ value: 25.113999999999997
1786
+ - type: precision_at_3
1787
+ value: 91.333
1788
+ - type: precision_at_5
1789
+ value: 90
1790
+ - type: recall_at_1
1791
+ value: 0.242
1792
+ - type: recall_at_10
1793
+ value: 2.267
1794
+ - type: recall_at_100
1795
+ value: 15.775
1796
+ - type: recall_at_1000
1797
+ value: 53.152
1798
+ - type: recall_at_3
1799
+ value: 0.721
1800
+ - type: recall_at_5
1801
+ value: 1.172
1802
+ - task:
1803
+ type: Retrieval
1804
+ dataset:
1805
+ type: webis-touche2020
1806
+ name: MTEB Touche2020
1807
+ config: default
1808
+ split: test
1809
+ revision: None
1810
+ metrics:
1811
+ - type: map_at_1
1812
+ value: 2.4619999999999997
1813
+ - type: map_at_10
1814
+ value: 10.086
1815
+ - type: map_at_100
1816
+ value: 16.265
1817
+ - type: map_at_1000
1818
+ value: 17.846
1819
+ - type: map_at_3
1820
+ value: 4.603
1821
+ - type: map_at_5
1822
+ value: 6.517
1823
+ - type: mrr_at_1
1824
+ value: 26.531
1825
+ - type: mrr_at_10
1826
+ value: 43.608000000000004
1827
+ - type: mrr_at_100
1828
+ value: 44.175
1829
+ - type: mrr_at_1000
1830
+ value: 44.190000000000005
1831
+ - type: mrr_at_3
1832
+ value: 37.755
1833
+ - type: mrr_at_5
1834
+ value: 41.531
1835
+ - type: ndcg_at_1
1836
+ value: 25.509999999999998
1837
+ - type: ndcg_at_10
1838
+ value: 25.663999999999998
1839
+ - type: ndcg_at_100
1840
+ value: 37.362
1841
+ - type: ndcg_at_1000
1842
+ value: 48.817
1843
+ - type: ndcg_at_3
1844
+ value: 23.223
1845
+ - type: ndcg_at_5
1846
+ value: 24.403
1847
+ - type: precision_at_1
1848
+ value: 26.531
1849
+ - type: precision_at_10
1850
+ value: 24.694
1851
+ - type: precision_at_100
1852
+ value: 7.776
1853
+ - type: precision_at_1000
1854
+ value: 1.541
1855
+ - type: precision_at_3
1856
+ value: 23.810000000000002
1857
+ - type: precision_at_5
1858
+ value: 25.306
1859
+ - type: recall_at_1
1860
+ value: 2.4619999999999997
1861
+ - type: recall_at_10
1862
+ value: 17.712
1863
+ - type: recall_at_100
1864
+ value: 48.232
1865
+ - type: recall_at_1000
1866
+ value: 83.348
1867
+ - type: recall_at_3
1868
+ value: 5.763
1869
+ - type: recall_at_5
1870
+ value: 9.577
1871
+ datasets:
1872
+ - Tevatron/msmarco-passage-corpus
1873
+ - Tevatron/msmarco-passage
1874
+ language:
1875
+ - en
1876
+ library_name: sentence-transformers
1877
+ pipeline_tag: sentence-similarity
1878
+ ---
1879
+
1880
+ # Phi2 Model Trained for retrieval task using MSMarco Dataset
1881
+ ### Trained for 1 epoch using the tevatron library
1882
+
1883
+ #### Ongoing work