Text Classification On Trec 6
평가 지표
Error
평가 결과
이 벤치마크에서 각 모델의 성능 결과
비교 표
| 모델 이름 | Error |
|---|---|
| vector-of-locally-aggregated-word-embeddings | 5.8 |
| delta-a-deep-learning-based-language | 7.8 |
| learned-in-translation-contextualized-word | 4.2 |
| message-passing-attention-networks-for | 6.2 |
| baseline-needs-more-love-on-simple-word | 7.8 |
| investigating-capsule-networks-with-dynamic | 7.2 |
| translations-as-additional-contexts-for | 4 |
| how-to-fine-tune-bert-for-text-classification | 3.2 |
| dual-contrastive-learning-text-classification | 2.60 |
| all-but-the-top-simple-and-effective | 7.0 |
| universal-language-model-fine-tuning-for-text | 3.6 |
| text-classification-improved-by-integrating | 3.9 |
| the-re-label-method-for-data-centric-machine | 0.40 |
| discriminative-neural-sentence-modeling-by | 4 |
| universal-sentence-encoder | 1.93 |
| distributed-word-representation-in-tsetlin | 9.96 |
| a-c-lstm-neural-network-for-text | 5.4 |
| a-la-carte-embedding-cheap-but-effective | 9.6 |
| 190600095 | 7.04 |