GRU-RNN-WORD2VEC

Reported on 2 benchmarks across 1 task · 1 paper · 1 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Natural Language Processing2 results

Sentiment AnalysisonMR
Accuracy· 2017-02-05
78.26
best: 93.3 (VLAWE)
SOTA
All-but-the-Top: Simple and Effective Postprocessing for Word Representations arXiv:1702.01417
Sentiment AnalysisonSST-5 Fine-grained classification
Accuracy· 2017-02-05
45.02
best: 62.27 (Llama-3.3-70B + CAPO)
All-but-the-Top: Simple and Effective Postprocessing for Word Representations arXiv:1702.01417