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الرئيسية
SOTA
الإجابة على الأسئلة
Question Answering On Squad11
Question Answering On Squad11
المقاييس
EM
F1
النتائج
نتائج أداء النماذج المختلفة على هذا المعيار القياسي
Columns
اسم النموذج
EM
F1
Paper Title
Repository
SAN (ensemble model)
79.608
86.496
Stochastic Answer Networks for Machine Reading Comprehension
S^3-Net (single model)
71.908
81.023
-
-
RQA (single model)
55.827
65.467
Harvesting and Refining Question-Answer Pairs for Unsupervised QA
PQMN (single model)
68.331
77.783
-
-
BERT - 3 Layers
77.7
85.8
Information Theoretic Representation Distillation
RuBERT
-
84.6
Adaptation of Deep Bidirectional Multilingual Transformers for Russian Language
BERT-uncased (single model)
84.926
91.932
-
-
{ANNA} (single model)
90.622
95.719
-
-
BISAN (single model)
85.314
91.756
-
-
Conductor-net (single model)
74.405
82.742
Phase Conductor on Multi-layered Attentions for Machine Comprehension
-
KACTEIL-MRC(GF-Net+) (single model)
78.664
85.780
-
-
BERT-Large 32k batch size with AdamW
-
91.58
A Large Batch Optimizer Reality Check: Traditional, Generic Optimizers Suffice Across Batch Sizes
-
FusionNet (single model)
75.968
83.900
FusionNet: Fusing via Fully-Aware Attention with Application to Machine Comprehension
WD (single model)
84.402
90.561
-
-
DyREX
-
91.01
DyREx: Dynamic Query Representation for Extractive Question Answering
WAHnGREA
0.000
0.000
-
-
S^3-Net (ensemble)
74.121
82.342
-
-
RaSoR + TR (single model)
75.789
83.261
Contextualized Word Representations for Reading Comprehension
RQA+IDR (single model)
61.145
71.389
Harvesting and Refining Question-Answer Pairs for Unsupervised QA
MEMEN (single model)
78.234
85.344
MEMEN: Multi-layer Embedding with Memory Networks for Machine Comprehension
-
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