Question Answering On Squad11

Metriken

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Vergleichstabelle

Modellname	EM	F1
stochastic-answer-networks-for-machine	79.608	86.496
Modell 2	71.908	81.023
harvesting-and-refining-question-answer-pairs	55.827	65.467
Modell 4	68.331	77.783
information-theoretic-representation	77.7	85.8
adaptation-of-deep-bidirectional-multilingual	-	84.6
Modell 7	84.926	91.932
Modell 8	90.622	95.719
Modell 9	85.314	91.756
phase-conductor-on-multi-layered-attentions	74.405	82.742
Modell 11	78.664	85.780
a-large-batch-optimizer-reality-check	-	91.58
fusionnet-fusing-via-fully-aware-attention	75.968	83.900
Modell 14	84.402	90.561
dyrex-dynamic-query-representation-for	-	91.01
Modell 16	0.000	0.000
Modell 17	74.121	82.342
contextualized-word-representations-for	75.789	83.261
harvesting-and-refining-question-answer-pairs	61.145	71.389
memen-multi-layer-embedding-with-memory	78.234	85.344
Modell 21	84.328	91.281
end-to-end-answer-chunk-extraction-and	62.499	70.956
Modell 23	81.003	87.432
multi-perspective-context-matching-for	73.765	81.257
Modell 25	55.827	65.467
Modell 26	86.458	92.645
exploring-machine-reading-comprehension-with	76.125	83.538
Modell 28	79.996	86.711
stochastic-answer-networks-for-machine	76.828	84.396
Modell 30	70.985	79.939
Modell 31	75.034	83.405
Modell 32	66.527	75.787
bert-pre-training-of-deep-bidirectional	87.4	93.2
Modell 34	75.265	82.769
Modell 35	71.016	79.835
information-theoretic-representation	81.5	88.5
making-neural-qa-as-simple-as-possible-but	70.849	78.857
Modell 38	69.600	78.236
Modell 39	59.058	69.436
Modell 40	76.859	84.739
Modell 41	72.485	80.550
multi-perspective-context-matching-for	70.387	78.784
Modell 43	85.356	91.202
bert-pre-training-of-deep-bidirectional	87.433	93.160
textbox-2-0-a-text-generation-library-with	-	93.04
learning-to-compute-word-embeddings-on-the	62.897	72.016
Modell 47	85.125	91.623
Modell 48	73.639	81.931
simple-and-effective-multi-paragraph-reading	72.139	81.048
Modell 50	61.145	71.389
dcn-mixed-objective-and-deep-residual	74.866	82.806
machine-comprehension-using-match-lstm-and	67.901	77.022
machine-comprehension-using-match-lstm-and	64.744	73.743
Modell 54	80.615	87.311
fusionnet-fusing-via-fully-aware-attention	78.978	86.016
Modell 56	79.199	86.590
Modell 57	81.790	88.163
words-or-characters-fine-grained-gating-for	62.446	73.327
Modell 59	83.426	89.218
efficientqa-a-roberta-based-phrase-indexed	74.9	83.1
Modell 61	47.341	56.436
Modell 62	0.000	0.000
Modell 63	80.027	87.288
Modell 64	52.544	62.780
learning-to-compute-word-embeddings-on-the	62.604	71.968
Modell 66	79.692	86.727
deep-contextualized-word-representations	81.003	87.432
spanbert-improving-pre-training-by	88.8	94.6
smarnet-teaching-machines-to-read-and	71.415	80.160
Modell 70	75.821	83.843
Modell 71	89.709	94.859
Modell 72	82.681	89.379
Modell 73	79.083	86.288
Modell 74	71.698	80.462
qanet-combining-local-convolution-with-global	76.2	84.6
making-neural-qa-as-simple-as-possible-but	68.436	77.070
reasonet-learning-to-stop-reading-in-machine	70.555	79.364
Modell 78	69.443	78.358
Modell 79	0.000	0.000
Modell 80	71.898	79.989
structural-embedding-of-syntactic-trees-for	74.090	81.761
Modell 82	67.618	77.151
Modell 83	83.982	89.796
Modell 84	80.436	87.021
Modell 85	72.758	81.001
Modell 86	0.000	0.000
Modell 87	82.482	89.281
Modell 88	89.646	94.930
memen-multi-layer-embedding-with-memory	75.370	82.658
Modell 90	77.237	84.466
Modell 91	85.944	92.425
Modell 92	78.087	85.348
Modell 93	88.912	94.584
bidirectional-attention-flow-for-machine	67.974	77.323
Modell 95	83.804	90.429
Modell 96	81.580	88.948
Modell 97	68.132	77.569
Modell 98	74.489	82.815
Modell 99	89.856	94.903
Modell 100	79.901	86.536
Modell 101	64.439	73.921
Modell 102	86.521	92.617
Modell 103	77.646	84.905
Modell 104	78.664	85.780
Modell 105	75.989	83.475
reading-wikipedia-to-answer-open-domain	70.733	79.353
structural-embedding-of-syntactic-trees-for	68.478	77.971
Modell 108	78.328	85.682
Modell 109	64.932	74.594
exploring-question-understanding-and	73.010	81.517
Modell 111	88.912	94.584
Modell 112	78.171	85.543
Modell 113	79.083	86.288
Modell 114	63.306	73.463
Modell 115	0.000	6.907
bidirectional-attention-flow-for-machine	73.744	81.525
Modell 117	78.496	85.469
exploring-question-understanding-and	70.607	79.821
Modell 119	79.031	86.006
a-multi-stage-memory-augmented-neural-network	79.692	86.727
deep-contextualized-word-representations	78.58	85.833
Modell 122	85.944	92.425
Modell 123	73.303	81.754
Modell 124	82.062	88.947
Modell 125	65.163	74.555
Modell 126	80.426	86.912
Modell 127	53.698	64.036
Modell 128	76.240	84.599
phase-conductor-on-multi-layered-attentions	73.240	81.933
xlnet-generalized-autoregressive-pretraining	89.898	95.080
bert-pre-training-of-deep-bidirectional	85.083	91.835
Modell 132	81.401	88.122
Modell 133	72.600	81.011
Modell 134	78.328	85.682
Modell 135	81.307	88.909
gated-self-matching-networks-for-reading	76.461	84.265
Modell 137	67.544	76.429
Modell 138	77.342	84.925
dynamic-coattention-networks-for-question	71.625	80.383
Modell 140	80.164	86.721
luke-deep-contextualized-entity	90.202	95.379
Modell 142	66.516	76.349
luke-deep-contextualized-entity	-	95.4
reinforced-mnemonic-reader-for-machine	82.283	88.533
Modell 145	88.650	94.393
Modell 146	76.461	84.265
Modell 147	0.000	0.000
Modell 148	77.845	85.297
Modell 149	80.720	87.758
Modell 150	75.223	82.716
Modell 151	90.202	95.379
Modell 152	76.775	84.491
Modell 153	83.468	90.133
Modell 154	44.215	54.723
machine-comprehension-using-match-lstm-and	60.474	70.695
Modell 156	79.083	86.450
a-fully-attention-based-information-retriever	67.744	77.605
dynamic-coattention-networks-for-question	66.233	75.896
Modell 159	75.926	83.305
Modell 160	85.430	91.976
Modell 161	76.146	83.991
reinforced-mnemonic-reader-for-machine	79.545	86.654
Modell 163	67.502	76.786
reinforced-mnemonic-reader-for-machine	70.995	80.146
reasonet-learning-to-stop-reading-in-machine	75.034	82.552
Modell 166	81.496	87.557
Modell 167	81.045	87.999
contextualized-word-representations-for	77.583	84.163
reinforced-mnemonic-reader-for-machine	74.268	82.371
Modell 170	80.667	88.169
Modell 171	74.080	81.665
Modell 172	71.373	79.725
Modell 173	78.401	85.724
Modell 174	87.465	93.294
Modell 175	78.653	86.663
Modell 176	72.590	81.415
luke-deep-contextualized-entity	90.2	-
Modell 178	12.273	13.211
Modell 179	74.604	82.501
phase-conductor-on-multi-layered-attentions	76.996	84.630
Modell 181	78.580	85.833
structural-embedding-of-syntactic-trees-for	73.723	81.530
Modell 183	82.849	88.764
Modell 184	89.898	95.080
bert-pre-training-of-deep-bidirectional	-	91.8
ruminating-reader-reasoning-with-gated-multi	70.639	79.456
structural-embedding-of-syntactic-trees-for	68.163	77.527
Modell 188	82.471	89.306
memen-multi-layer-embedding-with-memory	78.234	85.344
Modell 190	77.573	84.858
learning-to-compute-word-embeddings-on-the	64.083	73.056
linkbert-pretraining-language-models-with	87.45	92.7
Modell 193	78.223	85.535
Modell 194	88.839	94.635
Modell 195	77.090	83.931
Modell 196	84.978	92.019
learned-in-translation-contextualized-word	71.3	79.9
Modell 198	79.859	88.263
Modell 199	82.136	88.126
Modell 200	86.940	92.641
Modell 201	79.597	87.374
learning-recurrent-span-representations-for	70.849	78.741
simple-recurrent-units-for-highly	71.4	80.2
Modell 204	83.930	90.613
Modell 205	80.436	86.912
Modell 206	82.650	88.493
Modell 207	79.083	86.288
dcn-mixed-objective-and-deep-residual	78.852	85.996
Modell 209	80.489	87.454
Modell 210	82.440	88.607
Modell 211	85.335	91.807
Modell 212	52.533	62.757
machine-comprehension-using-match-lstm-and	54.505	67.748