| 训练集(数量) | 验证集(数量) | 测试集(数量) | |
|---|---|---|---|
| ATEC | 62477 | 20000 | 20000 |
| BQ | 100000 | 10000 | 10000 |
| LCQMC | 238766 | 8802 | 12500 |
| PAWSX | 49401 | 2000 | 2000 |
| STS-B | 5231 | 1458 | 1361 |
- 皮尔逊系数(pearsonr): 是衡量两个连续型变量的线性相关关系。
- 斯皮尔曼相关系数(spearmanr): 是衡量两变量之间的单调关系，两个变量同时变化，但是并非同样速率变化，即并非一定是线性关系。
没有专门去调参。 无监督的模型从训练集中随机采样了10000条数据。下面是在测试集上的结果。对最终结果影响比较大的就是学习率。尽可能的小就行。
| ATEC | BQ | LCQMC | PAWSX | STS-B | Avg | |
|---|---|---|---|---|---|---|
| Word2Vec (unsup) | 16.4936 | 24.6732 | 29.8313 | 7.4375 | 23.5855 | 20.4042 |
| SimCSE (unsup) | 30.8634 | 49.1813 | 68.9802 | 9.5895 | 71.3976 | 46.0024 |
| PromptBERT (unsup) | 34.9434 | 48.7067 | 67.7634 | 14.3244 | 71.4191 | 47.4314 |
| GS-infoNCE (unsup) | 28.9731 | 46.3247 | 67.3204 | 11.2317 | 73.2998 | 45.4299 |
| ESimCSE (unsup) | 31.8443 | 48.0718 | 66.8673 | 9.1819 | 65.1843 | 44.2299 |
| ConSERT (unsup) | 29.7437 | 46.7806 | 67.5121 | 8.1442 | 74.1097 | 45.2580 |
| SentenceBert (sup) | 48.5157 | 67.8545 | 79.6023 | 60.1675 | 71.0148 | 65.4309 |
| CoSENT (sup) | 50.5969 | 72.5191 | 79.3777 | 60.5475 | 80.4344 | 68.6951 |
| SimCSE (sup) | ** | ** | ** | ** | ** | ** |
| ATEC | BQ | LCQMC | PAWSX | STS-B | Avg | |
|---|---|---|---|---|---|---|
| Word2Vec (unsup) | 14.2917 | 18.1433 | 24.7312 | 10.6328 | 12.9765 | 16.1551 |
| SimCSE (unsup) | 33.1678 | 49.0413 | 57.5075 | 9.9956 | 72.8918 | 44.5207 |
| PromptBERT (unsup) | 35.6218 | 48.6450 | 59.8181 | 13.5495 | 71.7247 | 45.8718 |
| GS-infoNCE (unsup) | 30.3781 | 46.2700 | 57.2458 | 10.3298 | 74.4048 | 43.7257 |
| ESimCSE (unsup) | 32.6815 | 47.9271 | 52.8407 | 10.5426 | 65.2000 | 41.8383 |
| ConSERT (unsup) | 31.1873 | 46.6954 | 60.7141 | 8.2408 | 75.3964 | 44.4468 |
| SentenceBert (sup) | 45.4922 | 66.3670 | 75.2732 | 57.7105 | 71.4540 | 63.2593 |
| CoSENT (sup) | 50.4301 | 72.5830 | 77.6607 | 57.6305 | 78.5165 | 67.3641 |
| SimCSE (sup) | ** | ** | ** | ** | ** | ** |
注
- Word2Vec模型没有在大规模的语料上训练，只是在训练集上训练过，然后再测试集上做的推理。
- 上述无监督的深度学习模型都采用的是“CLS”向量