Sentiment Analysis On Mr
Metriken
Accuracy
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Vergleichstabelle
| Modellname | Accuracy |
|---|---|
| vector-of-locally-aggregated-word-embeddings | 93.3 |
| entailment-as-few-shot-learner | 92.5 |
| simplifying-graph-convolutional-networks | 75.9 |
| simplifying-graph-convolutional-networks | 75.9 |
| sentiment-analysis-by-capsules | 83.8 |
| a-la-carte-embedding-cheap-but-effective | 86.8 |
| sentence-state-lstm-for-text-representation | 76.2 |
| distributed-word-representation-in-tsetlin | 77.51 |
| a-multi-sentiment-resource-enhanced-attention | 84.5 |
| improved-sentence-modeling-using-suffix | 81.6 |
| using-millions-of-emoji-occurrences-to-learn | - |
| angle-optimized-text-embeddings | 91.09 |
| baseline-needs-more-love-on-simple-word | 78.2 |
| graph-star-net-for-generalized-multi-task-1 | 76.6 |
| graph-convolutional-networks-for-text | 76.74 |
| all-but-the-top-simple-and-effective | 78.26 |
| investigating-capsule-networks-with-dynamic | 82.3 |
| 190600095 | 80.09 |
| universal-sentence-encoder | 81.59 |