HyperAI
HyperAI
Startseite
Plattform
Dokumentation
Neuigkeiten
Forschungsarbeiten
Tutorials
Datensätze
Wiki
SOTA
LLM-Modelle
GPU-Rangliste
Veranstaltungen
Suche
Über
Nutzungsbedingungen
Datenschutzrichtlinie
Deutsch
HyperAI
HyperAI
Toggle Sidebar
Seite durchsuchen…
⌘
K
Command Palette
Search for a command to run...
Plattform
Startseite
SOTA
Sentimentanalyse
Sentiment Analysis On Sst 2 Binary
Sentiment Analysis On Sst 2 Binary
Metriken
Accuracy
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Columns
Modellname
Accuracy
Paper Title
T5-11B
97.5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
MT-DNN-SMART
97.5
SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization
T5-3B
97.4
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
MUPPET Roberta Large
97.4
Muppet: Massive Multi-task Representations with Pre-Finetuning
StructBERTRoBERTa ensemble
97.1
StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding
ALBERT
97.1
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
XLNet (single model)
97
XLNet: Generalized Autoregressive Pretraining for Language Understanding
ELECTRA
96.9
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
RoBERTa-large 355M + Entailment as Few-shot Learner
96.9
Entailment as Few-Shot Learner
XLNet-Large (ensemble)
96.8
XLNet: Generalized Autoregressive Pretraining for Language Understanding
FLOATER-large
96.7
Learning to Encode Position for Transformer with Continuous Dynamical Model
MUPPET Roberta base
96.7
Muppet: Massive Multi-task Representations with Pre-Finetuning
RoBERTa (ensemble)
96.7
RoBERTa: A Robustly Optimized BERT Pretraining Approach
DeBERTa (large)
96.5
DeBERTa: Decoding-enhanced BERT with Disentangled Attention
MT-DNN-ensemble
96.5
Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding
RoBERTa-large 355M (MLP quantized vector-wise, fine-tuned)
96.4
LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
ASA + RoBERTa
96.3
Adversarial Self-Attention for Language Understanding
T5-Large 770M
96.3
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Snorkel MeTaL(ensemble)
96.2
Training Complex Models with Multi-Task Weak Supervision
PSQ (Chen et al., 2020)
96.2
A Statistical Framework for Low-bitwidth Training of Deep Neural Networks
0 of 88 row(s) selected.
Previous
Next
Sentiment Analysis On Sst 2 Binary | SOTA | HyperAI