--- license: apache-2.0 base_model: google/t5-efficient-tiny tags: - generated_from_trainer metrics: - rouge model-index: - name: denoice-finetuned-xsum results: [] --- # denoice-finetuned-xsum This model is a fine-tuned version of [google/t5-efficient-tiny](https://huggingface.co/google/t5-efficient-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0178 - Rouge1: 95.5056 - Rouge2: 72.8464 - Rougel: 95.3933 - Rougelsum: 95.5056 - Gen Len: 5.1517 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 500 - eval_batch_size: 500 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 70 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 36 | 0.0219 | 94.8315 | 72.6592 | 94.8315 | 94.8876 | 5.1348 | | No log | 2.0 | 72 | 0.0218 | 94.8315 | 72.6592 | 94.8315 | 94.8876 | 5.1348 | | No log | 3.0 | 108 | 0.0215 | 94.8315 | 72.6592 | 94.8315 | 94.8876 | 5.1348 | | No log | 4.0 | 144 | 0.0215 | 95.0562 | 72.6592 | 95.0562 | 95.0562 | 5.1573 | | No log | 5.0 | 180 | 0.0214 | 95.0562 | 72.6592 | 95.0562 | 95.0562 | 5.1517 | | No log | 6.0 | 216 | 0.0212 | 94.8315 | 72.6592 | 94.8315 | 94.8876 | 5.1348 | | No log | 7.0 | 252 | 0.0209 | 94.6067 | 72.6592 | 94.6067 | 94.6067 | 5.1292 | | No log | 8.0 | 288 | 0.0210 | 94.8876 | 72.0974 | 94.7753 | 94.8315 | 5.1236 | | No log | 9.0 | 324 | 0.0208 | 94.8876 | 72.0974 | 94.7753 | 94.8315 | 5.1236 | | No log | 10.0 | 360 | 0.0210 | 95.1124 | 72.0974 | 95.0 | 95.1124 | 5.1404 | | No log | 11.0 | 396 | 0.0207 | 95.6742 | 72.6592 | 95.618 | 95.6742 | 5.1573 | | No log | 12.0 | 432 | 0.0207 | 95.1124 | 72.0974 | 95.0 | 95.1124 | 5.1461 | | No log | 13.0 | 468 | 0.0206 | 95.1124 | 72.0974 | 95.0 | 95.1124 | 5.1404 | | 0.0349 | 14.0 | 504 | 0.0203 | 95.3933 | 72.6592 | 95.2809 | 95.3933 | 5.1461 | | 0.0349 | 15.0 | 540 | 0.0202 | 95.3933 | 72.6592 | 95.2809 | 95.3933 | 5.1461 | | 0.0349 | 16.0 | 576 | 0.0201 | 95.1124 | 72.0974 | 95.0 | 95.1124 | 5.1404 | | 0.0349 | 17.0 | 612 | 0.0201 | 95.1124 | 72.0974 | 95.0 | 95.1124 | 5.1404 | | 0.0349 | 18.0 | 648 | 0.0196 | 95.1124 | 72.0974 | 95.0 | 95.1124 | 5.1404 | | 0.0349 | 19.0 | 684 | 0.0194 | 95.2247 | 72.2846 | 95.1124 | 95.2247 | 5.1629 | | 0.0349 | 20.0 | 720 | 0.0192 | 95.1124 | 72.0974 | 95.0 | 95.1124 | 5.1404 | | 0.0349 | 21.0 | 756 | 0.0192 | 95.3933 | 72.6592 | 95.2809 | 95.3933 | 5.1461 | | 0.0349 | 22.0 | 792 | 0.0193 | 95.3933 | 72.6592 | 95.2809 | 95.3933 | 5.1461 | | 0.0349 | 23.0 | 828 | 0.0193 | 95.5056 | 72.8464 | 95.3933 | 95.5056 | 5.1517 | | 0.0349 | 24.0 | 864 | 0.0194 | 95.3933 | 72.6592 | 95.2809 | 95.3933 | 5.1461 | | 0.0349 | 25.0 | 900 | 0.0193 | 95.3933 | 72.6592 | 95.2809 | 95.3933 | 5.1461 | | 0.0349 | 26.0 | 936 | 0.0194 | 95.3933 | 72.6592 | 95.2809 | 95.3933 | 5.1461 | | 0.0349 | 27.0 | 972 | 0.0193 | 95.3933 | 72.6592 | 95.2809 | 95.3933 | 5.1461 | | 0.0315 | 28.0 | 1008 | 0.0192 | 95.3933 | 72.6592 | 95.2809 | 95.3933 | 5.1461 | | 0.0315 | 29.0 | 1044 | 0.0190 | 95.3933 | 72.6592 | 95.2809 | 95.3933 | 5.1461 | | 0.0315 | 30.0 | 1080 | 0.0191 | 95.3933 | 72.6592 | 95.2809 | 95.3933 | 5.1461 | | 0.0315 | 31.0 | 1116 | 0.0190 | 95.3933 | 72.6592 | 95.2809 | 95.3933 | 5.1461 | | 0.0315 | 32.0 | 1152 | 0.0191 | 95.3933 | 72.6592 | 95.2809 | 95.3933 | 5.1461 | | 0.0315 | 33.0 | 1188 | 0.0190 | 95.1124 | 72.0974 | 95.0 | 95.1124 | 5.1404 | | 0.0315 | 34.0 | 1224 | 0.0190 | 95.1124 | 72.0974 | 95.0 | 95.1124 | 5.1404 | | 0.0315 | 35.0 | 1260 | 0.0188 | 95.1124 | 72.0974 | 95.0 | 95.1124 | 5.1404 | | 0.0315 | 36.0 | 1296 | 0.0187 | 95.1124 | 72.0974 | 95.0 | 95.1124 | 5.1404 | | 0.0315 | 37.0 | 1332 | 0.0186 | 95.1124 | 72.0974 | 95.0 | 95.1124 | 5.1404 | | 0.0315 | 38.0 | 1368 | 0.0186 | 95.2247 | 72.2846 | 95.1124 | 95.2247 | 5.1461 | | 0.0315 | 39.0 | 1404 | 0.0186 | 95.5056 | 72.8464 | 95.3933 | 95.5056 | 5.1517 | | 0.0315 | 40.0 | 1440 | 0.0186 | 95.3933 | 72.6592 | 95.2809 | 95.3933 | 5.1461 | | 0.0315 | 41.0 | 1476 | 0.0185 | 95.5056 | 72.8464 | 95.3933 | 95.5056 | 5.1517 | | 0.0291 | 42.0 | 1512 | 0.0184 | 95.5056 | 72.8464 | 95.3933 | 95.5056 | 5.1517 | | 0.0291 | 43.0 | 1548 | 0.0184 | 95.5056 | 72.8464 | 95.3933 | 95.5056 | 5.1517 | | 0.0291 | 44.0 | 1584 | 0.0184 | 95.5056 | 72.8464 | 95.3933 | 95.5056 | 5.1517 | | 0.0291 | 45.0 | 1620 | 0.0183 | 95.618 | 73.0337 | 95.5056 | 95.618 | 5.1742 | | 0.0291 | 46.0 | 1656 | 0.0182 | 95.5056 | 72.8464 | 95.3933 | 95.5056 | 5.1517 | | 0.0291 | 47.0 | 1692 | 0.0182 | 95.618 | 73.0337 | 95.5056 | 95.618 | 5.1742 | | 0.0291 | 48.0 | 1728 | 0.0182 | 95.618 | 73.0337 | 95.5056 | 95.618 | 5.1742 | | 0.0291 | 49.0 | 1764 | 0.0182 | 95.618 | 73.0337 | 95.5056 | 95.618 | 5.1742 | | 0.0291 | 50.0 | 1800 | 0.0182 | 95.618 | 73.0337 | 95.5056 | 95.618 | 5.1742 | | 0.0291 | 51.0 | 1836 | 0.0182 | 95.618 | 73.0337 | 95.5056 | 95.618 | 5.1742 | | 0.0291 | 52.0 | 1872 | 0.0181 | 95.618 | 73.0337 | 95.5056 | 95.618 | 5.1742 | | 0.0291 | 53.0 | 1908 | 0.0179 | 95.2247 | 72.2846 | 95.1124 | 95.2247 | 5.1461 | | 0.0291 | 54.0 | 1944 | 0.0179 | 95.5056 | 72.8464 | 95.3933 | 95.5056 | 5.1517 | | 0.0291 | 55.0 | 1980 | 0.0179 | 95.618 | 73.0337 | 95.5056 | 95.618 | 5.1742 | | 0.0279 | 56.0 | 2016 | 0.0178 | 95.5056 | 72.8464 | 95.3933 | 95.5056 | 5.1517 | | 0.0279 | 57.0 | 2052 | 0.0178 | 95.5056 | 72.8464 | 95.3933 | 95.5056 | 5.1517 | | 0.0279 | 58.0 | 2088 | 0.0177 | 95.5056 | 72.8464 | 95.3933 | 95.5056 | 5.1517 | | 0.0279 | 59.0 | 2124 | 0.0178 | 95.5056 | 72.8464 | 95.3933 | 95.5056 | 5.1517 | | 0.0279 | 60.0 | 2160 | 0.0178 | 95.5056 | 72.8464 | 95.3933 | 95.5056 | 5.1517 | | 0.0279 | 61.0 | 2196 | 0.0178 | 95.5056 | 72.8464 | 95.3933 | 95.5056 | 5.1517 | | 0.0279 | 62.0 | 2232 | 0.0178 | 95.5056 | 72.8464 | 95.3933 | 95.5056 | 5.1517 | | 0.0279 | 63.0 | 2268 | 0.0178 | 95.5056 | 72.8464 | 95.3933 | 95.5056 | 5.1517 | | 0.0279 | 64.0 | 2304 | 0.0178 | 95.5056 | 72.8464 | 95.3933 | 95.5056 | 5.1517 | | 0.0279 | 65.0 | 2340 | 0.0178 | 95.5056 | 72.8464 | 95.3933 | 95.5056 | 5.1517 | | 0.0279 | 66.0 | 2376 | 0.0178 | 95.5056 | 72.8464 | 95.3933 | 95.5056 | 5.1517 | | 0.0279 | 67.0 | 2412 | 0.0178 | 95.5056 | 72.8464 | 95.3933 | 95.5056 | 5.1517 | | 0.0279 | 68.0 | 2448 | 0.0178 | 95.5056 | 72.8464 | 95.3933 | 95.5056 | 5.1517 | | 0.0279 | 69.0 | 2484 | 0.0178 | 95.5056 | 72.8464 | 95.3933 | 95.5056 | 5.1517 | | 0.0274 | 70.0 | 2520 | 0.0178 | 95.5056 | 72.8464 | 95.3933 | 95.5056 | 5.1517 | ### Framework versions - Transformers 4.36.2 - Pytorch 1.13.1 - Datasets 2.16.1 - Tokenizers 0.15.0