paper-with-me

Sentiment Analysis 벤치마크

Sentiment Analysis on IMDb

52개 결과 · ⬇ CSV · JSON

Accuracy

30.3 46.99 63.69 80.38 97.07 2014-12 2026-09 seq2-bown-CNN — 92.33 (2014-12-01) oh-LSTM — 94.1 (2016-02-07) Virtual adversarial training — 94.1 (2016-05-25) CEN-tpc — 94.52 (2017-05-29) CNN+LSTM — 88.9 (2017-07-06) Doc2VecC — 88.3 (2017-07-08) BCN+Char+CoVe — 91.8 (2017-08-01) Block-sparse LSTM — 94.99 (2017-12-01) ULMFiT — 95.4 (2018-01-18) S-LSTM — 87.15 (2018-05-07) Standard DR-AGG — 45.1 (2018-06-05) Reverse DR-AGG — 44.5 (2018-06-05) ToWE-SG — 90.8 (2018-08-01) LSTM with dynamic skip — 90.1 (2018-11-09) GPT-2 Finetuned — 92.36 (2019-02-14) BERT large finetune UDA — 95.8 (2019-04-29) BERT large — 95.49 (2019-04-29) BERT_large+ITPT — 95.79 (2019-05-14) BERT_base+ITPT — 95.63 (2019-05-14) XLNet — 96.21 (2019-06-19) GraphStar — 96.0 (2019-06-21) DV-ngrams-cosine — 93.13 (2019-07-01) DistilBERT 66M — 92.82 (2019-10-02) BP-Transformer + GloVe — 92.12 (2019-11-11) L MIXED — 95.68 (2020-09-08) coRNN — 87.4 (2020-10-02) COSINE — 90.54 (2020-10-15) Nyströmformer — 93.2 (2021-02-07) Modified LMU (34M) — 93.2 (2021-02-22) UnICORNN — 88.4 (2021-03-09) RoBERTa-large 355M + Entailment as Few-shot Learner — 96.1 (2021-04-29) AlexNet [alexnet] — 87.0 (2021-06-23) VGG16 [vgg16] — 86.0 (2021-06-23) ResNext[resnext] — 85.0 (2021-06-23) CfC — 88.4 (2021-06-25) FLAN 137B (few-shot, k=2) — 95.0 (2021-09-03) FLAN 137B (zero-shot) — 94.3 (2021-09-03) DV-ngrams-cosine with NB sub-sampling + RoBERTa.base — 95.94 (2022-05-26) DV-ngrams-cosine + RoBERTa.base — 95.92 (2022-05-26) RoBERTa.base — 95.79 (2022-05-26) DV-ngrams-cosine + NB-weighted BON (re-evaluated) — 93.68 (2022-05-26) Heinsen Routing + RoBERTa Large — 96.2 (2022-11-20) OCaTS (kNN & GPT-3.5-turbo — 93.06 (2023-10-20) Space-XLNet — 94.88 (2024-01-30) RoBERTa-large with LlamBERT — 96.68 (2024-03-23) RoBERTa-large — 96.54 (2024-03-23) Llama-2-70b-chat (0-shot) — 95.39 (2024-03-23) Bert+ Wilson-Cowan model RNN — 87.46 (2024-06-24) Enhancing Sentiment Classification with — 97.072 (2025-10-30) CISEA-MRFE — 30.3 (2025-11-01) Enhancing Hyperspace Analogue to Languag — 82.38 (2026-03-20) seq2-bown-CNN — 92.33 (2014-12-01) oh-LSTM — 94.1 (2016-02-07) CEN-tpc — 94.52 (2017-05-29) Block-sparse LSTM — 94.99 (2017-12-01) ULMFiT — 95.4 (2018-01-18) BERT large finetune UDA — 95.8 (2019-04-29) XLNet — 96.21 (2019-06-19) RoBERTa-large with LlamBERT — 96.68 (2024-03-23) Enhancing Sentiment Classification with — 97.072 (2025-10-30)
RankModel Accuracy Extra Training Data PaperCodeYear
1 Enhancing Sentiment Classification with 자동 추출 97.072 Enhancing Sentiment Classification with Machine Learning and Combinatorial Fusion 2025
2 RoBERTa-large with LlamBERT 96.68 LlamBERT: Large-scale low-cost data annotation in NLP aielte-research/llambert 2024
3 RoBERTa-large 96.54 LlamBERT: Large-scale low-cost data annotation in NLP aielte-research/llambert 2024
4 XLNet 96.21 ✓ XLNet: Generalized Autoregressive Pretraining for Language Understanding huggingface/transformers · PaddlePaddle/PaddleNLP · zihangdai/xlnet · +24 2019
5 Heinsen Routing + RoBERTa Large 96.2 An Algorithm for Routing Vectors in Sequences glassroom/heinsen_routing 2022
6 RoBERTa-large 355M + Entailment as Few-shot Learner 96.1 Entailment as Few-Shot Learner PaddlePaddle/PaddleNLP · sunyilgdx/prompts4keras · cactilab/hateguard 2021
7 GraphStar 96.0 Graph Star Net for Generalized Multi-Task Learning graph-star-team/graph_star 2019
8 DV-ngrams-cosine with NB sub-sampling + RoBERTa.base 95.94 The Document Vectors Using Cosine Similarity Revisited bgzh/dv_cosine_revisited 2022
9 DV-ngrams-cosine + RoBERTa.base 95.92 The Document Vectors Using Cosine Similarity Revisited bgzh/dv_cosine_revisited 2022
10 Roberta_Large ST + Cosine Similarity Loss 95.9
11 BERT large finetune UDA 95.8 ✓ Unsupervised Data Augmentation for Consistency Training google-research/uda · SanghunYun/UDA_pytorch · ildoonet/unsupervised-data-augmentation · +17 2019
12 BERT_large+ITPT 95.79 How to Fine-Tune BERT for Text Classification? xuyige/BERT4doc-Classification · ongunuzaymacar/comparatively-finetuning-bert · uzaymacar/comparatively-finetuning-bert · +12 2019
12 RoBERTa.base 95.79 ✓ The Document Vectors Using Cosine Similarity Revisited bgzh/dv_cosine_revisited 2022
14 L MIXED 95.68 Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Function DevSinghSachan/ssl_text_classification 2020
15 BERT_base+ITPT 95.63 How to Fine-Tune BERT for Text Classification? xuyige/BERT4doc-Classification · ongunuzaymacar/comparatively-finetuning-bert · uzaymacar/comparatively-finetuning-bert · +12 2019
16 BERT large 95.49 Unsupervised Data Augmentation for Consistency Training google-research/uda · SanghunYun/UDA_pytorch · ildoonet/unsupervised-data-augmentation · +17 2019
17 ULMFiT 95.4 Universal Language Model Fine-tuning for Text Classification fastai/fastai · mrdbourke/tensorflow-deep-learning · Socialbird-AILab/BERT-Classification-Tutorial · +63 2018
18 Llama-2-70b-chat (0-shot) 95.39 ✓ LlamBERT: Large-scale low-cost data annotation in NLP aielte-research/llambert 2024
19 FLAN 137B (few-shot, k=2) 95 ✓ Finetuned Language Models Are Zero-Shot Learners hiyouga/llama-efficient-tuning · bigcode-project/starcoder · bigscience-workshop/promptsource · +5 2021
20 Block-sparse LSTM 94.99 GPU Kernels for Block-Sparse Weights openai/blocksparse 2017
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