--- license: apache-2.0 base_model: google/t5-v1_1-large tags: - generated_from_trainer model-index: - name: Sentiment-google-t5-v1_1-large-inter_model-dataset-frequency-human_annots_str results: [] --- # Sentiment-google-t5-v1_1-large-inter_model-dataset-frequency-human_annots_str This model is a fine-tuned version of [google/t5-v1_1-large](https://huggingface.co./google/t5-v1_1-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0889 ## 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: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 20.8833 | 1.0 | 44 | 25.4149 | | 17.9582 | 2.0 | 88 | 15.2004 | | 12.4496 | 3.0 | 132 | 11.1146 | | 10.6482 | 4.0 | 176 | 10.7774 | | 10.0038 | 5.0 | 220 | 10.5784 | | 9.8548 | 6.0 | 264 | 10.4290 | | 9.7749 | 7.0 | 308 | 10.2568 | | 9.4275 | 8.0 | 352 | 9.8102 | | 8.8894 | 9.0 | 396 | 9.2370 | | 8.4944 | 10.0 | 440 | 8.9575 | | 8.4109 | 11.0 | 484 | 8.7954 | | 8.3217 | 12.0 | 528 | 8.6723 | | 7.8791 | 13.0 | 572 | 8.5306 | | 1.0442 | 14.0 | 616 | 0.8718 | | 0.9076 | 15.0 | 660 | 0.8507 | | 0.9013 | 16.0 | 704 | 0.8517 | | 0.9 | 17.0 | 748 | 0.8475 | | 0.8835 | 18.0 | 792 | 0.8480 | | 0.8842 | 19.0 | 836 | 0.8525 | | 0.8836 | 20.0 | 880 | 0.8532 | | 0.8845 | 21.0 | 924 | 0.8458 | | 0.8941 | 22.0 | 968 | 0.8485 | | 0.8819 | 23.0 | 1012 | 0.8450 | | 0.8921 | 24.0 | 1056 | 0.8513 | | 0.888 | 25.0 | 1100 | 0.8444 | | 0.8857 | 26.0 | 1144 | 0.8486 | | 0.8737 | 27.0 | 1188 | 0.8424 | | 0.8766 | 28.0 | 1232 | 0.8423 | | 0.8746 | 29.0 | 1276 | 0.8430 | | 0.8829 | 30.0 | 1320 | 0.8402 | | 0.862 | 31.0 | 1364 | 0.8366 | | 0.8601 | 32.0 | 1408 | 0.8386 | | 0.8658 | 33.0 | 1452 | 0.8326 | | 0.8737 | 34.0 | 1496 | 0.8342 | | 0.8662 | 35.0 | 1540 | 0.8309 | | 0.8722 | 36.0 | 1584 | 0.8290 | | 0.8682 | 37.0 | 1628 | 0.8216 | | 0.859 | 38.0 | 1672 | 0.8430 | | 0.8554 | 39.0 | 1716 | 0.8170 | | 0.8565 | 40.0 | 1760 | 0.8114 | | 0.8402 | 41.0 | 1804 | 0.8079 | | 0.848 | 42.0 | 1848 | 0.8178 | | 0.8458 | 43.0 | 1892 | 0.8123 | | 0.842 | 44.0 | 1936 | 0.8026 | | 0.8259 | 45.0 | 1980 | 0.7977 | | 0.8313 | 46.0 | 2024 | 0.7931 | | 0.8418 | 47.0 | 2068 | 0.7935 | | 0.8253 | 48.0 | 2112 | 0.7892 | | 0.8251 | 49.0 | 2156 | 0.7851 | | 0.8153 | 50.0 | 2200 | 0.7833 | | 0.809 | 51.0 | 2244 | 0.7822 | | 0.8137 | 52.0 | 2288 | 0.7759 | | 0.8152 | 53.0 | 2332 | 0.7781 | | 0.8201 | 54.0 | 2376 | 0.7812 | | 0.8049 | 55.0 | 2420 | 0.7795 | | 0.8003 | 56.0 | 2464 | 0.7730 | | 0.797 | 57.0 | 2508 | 0.7692 | | 0.8031 | 58.0 | 2552 | 0.7696 | | 0.7907 | 59.0 | 2596 | 0.7685 | | 0.7935 | 60.0 | 2640 | 0.7612 | | 0.7958 | 61.0 | 2684 | 0.7558 | | 0.7932 | 62.0 | 2728 | 0.7539 | | 0.7944 | 63.0 | 2772 | 0.7510 | | 0.7952 | 64.0 | 2816 | 0.7500 | | 0.7838 | 65.0 | 2860 | 0.7551 | | 0.7797 | 66.0 | 2904 | 0.7417 | | 0.7821 | 67.0 | 2948 | 0.7399 | | 0.7739 | 68.0 | 2992 | 0.7440 | | 0.7697 | 69.0 | 3036 | 0.7331 | | 0.7709 | 70.0 | 3080 | 0.7343 | | 0.7679 | 71.0 | 3124 | 0.7389 | | 0.7506 | 72.0 | 3168 | 0.7296 | | 0.7645 | 73.0 | 3212 | 0.7336 | | 0.7501 | 74.0 | 3256 | 0.7278 | | 0.7602 | 75.0 | 3300 | 0.7268 | | 0.7475 | 76.0 | 3344 | 0.7221 | | 0.7561 | 77.0 | 3388 | 0.7211 | | 0.7534 | 78.0 | 3432 | 0.7135 | | 0.7582 | 79.0 | 3476 | 0.7173 | | 0.734 | 80.0 | 3520 | 0.7096 | | 0.7481 | 81.0 | 3564 | 0.7094 | | 0.7454 | 82.0 | 3608 | 0.7053 | | 0.7408 | 83.0 | 3652 | 0.6956 | | 0.7189 | 84.0 | 3696 | 0.6943 | | 0.7467 | 85.0 | 3740 | 0.6997 | | 0.7544 | 86.0 | 3784 | 0.7049 | | 0.7221 | 87.0 | 3828 | 0.6903 | | 0.7358 | 88.0 | 3872 | 0.6851 | | 0.727 | 89.0 | 3916 | 0.6807 | | 0.7127 | 90.0 | 3960 | 0.6828 | | 0.7158 | 91.0 | 4004 | 0.6837 | | 0.7284 | 92.0 | 4048 | 0.6818 | | 0.7153 | 93.0 | 4092 | 0.6906 | | 0.7172 | 94.0 | 4136 | 0.6804 | | 0.7076 | 95.0 | 4180 | 0.6694 | | 0.7009 | 96.0 | 4224 | 0.6722 | | 0.6915 | 97.0 | 4268 | 0.6775 | | 0.6997 | 98.0 | 4312 | 0.6596 | | 0.6924 | 99.0 | 4356 | 0.6595 | | 0.704 | 100.0 | 4400 | 0.6598 | | 0.6889 | 101.0 | 4444 | 0.6504 | | 0.6932 | 102.0 | 4488 | 0.6570 | | 0.6847 | 103.0 | 4532 | 0.6477 | | 0.6851 | 104.0 | 4576 | 0.6408 | | 0.6843 | 105.0 | 4620 | 0.6392 | | 0.6925 | 106.0 | 4664 | 0.6330 | | 0.6648 | 107.0 | 4708 | 0.6289 | | 0.6744 | 108.0 | 4752 | 0.6258 | | 0.6752 | 109.0 | 4796 | 0.6439 | | 0.6729 | 110.0 | 4840 | 0.6228 | | 0.6649 | 111.0 | 4884 | 0.6388 | | 0.6567 | 112.0 | 4928 | 0.6248 | | 0.6556 | 113.0 | 4972 | 0.6196 | | 0.6607 | 114.0 | 5016 | 0.6133 | | 0.6487 | 115.0 | 5060 | 0.6235 | | 0.6636 | 116.0 | 5104 | 0.6159 | | 0.6625 | 117.0 | 5148 | 0.6030 | | 0.6363 | 118.0 | 5192 | 0.6072 | | 0.6504 | 119.0 | 5236 | 0.5983 | | 0.6406 | 120.0 | 5280 | 0.6009 | | 0.6283 | 121.0 | 5324 | 0.5955 | | 0.612 | 122.0 | 5368 | 0.5883 | | 0.6295 | 123.0 | 5412 | 0.5879 | | 0.6392 | 124.0 | 5456 | 0.5848 | | 0.6144 | 125.0 | 5500 | 0.5814 | | 0.6204 | 126.0 | 5544 | 0.5856 | | 0.6144 | 127.0 | 5588 | 0.5826 | | 0.6119 | 128.0 | 5632 | 0.5788 | | 0.6125 | 129.0 | 5676 | 0.5814 | | 0.6093 | 130.0 | 5720 | 0.5729 | | 0.6035 | 131.0 | 5764 | 0.5702 | | 0.6227 | 132.0 | 5808 | 0.5663 | | 0.6287 | 133.0 | 5852 | 0.5608 | | 0.6092 | 134.0 | 5896 | 0.5554 | | 0.6158 | 135.0 | 5940 | 0.5507 | | 0.6113 | 136.0 | 5984 | 0.5555 | | 0.5976 | 137.0 | 6028 | 0.5547 | | 0.595 | 138.0 | 6072 | 0.5436 | | 0.5891 | 139.0 | 6116 | 0.5417 | | 0.583 | 140.0 | 6160 | 0.5375 | | 0.5915 | 141.0 | 6204 | 0.5304 | | 0.5855 | 142.0 | 6248 | 0.5253 | | 0.5875 | 143.0 | 6292 | 0.5364 | | 0.581 | 144.0 | 6336 | 0.5245 | | 0.5806 | 145.0 | 6380 | 0.5220 | | 0.5589 | 146.0 | 6424 | 0.5150 | | 0.573 | 147.0 | 6468 | 0.5252 | | 0.5843 | 148.0 | 6512 | 0.5169 | | 0.5705 | 149.0 | 6556 | 0.5156 | | 0.5756 | 150.0 | 6600 | 0.5208 | | 0.5575 | 151.0 | 6644 | 0.5028 | | 0.5574 | 152.0 | 6688 | 0.5049 | | 0.5598 | 153.0 | 6732 | 0.5054 | | 0.5571 | 154.0 | 6776 | 0.5096 | | 0.5673 | 155.0 | 6820 | 0.5012 | | 0.5634 | 156.0 | 6864 | 0.4902 | | 0.5601 | 157.0 | 6908 | 0.4949 | | 0.5423 | 158.0 | 6952 | 0.4851 | | 0.5568 | 159.0 | 6996 | 0.5020 | | 0.5664 | 160.0 | 7040 | 0.4846 | | 0.5523 | 161.0 | 7084 | 0.4865 | | 0.5502 | 162.0 | 7128 | 0.4797 | | 0.5374 | 163.0 | 7172 | 0.4735 | | 0.557 | 164.0 | 7216 | 0.4784 | | 0.5481 | 165.0 | 7260 | 0.4771 | | 0.5509 | 166.0 | 7304 | 0.4688 | | 0.5285 | 167.0 | 7348 | 0.4849 | | 0.5312 | 168.0 | 7392 | 0.4741 | | 0.5383 | 169.0 | 7436 | 0.4645 | | 0.5413 | 170.0 | 7480 | 0.4724 | | 0.524 | 171.0 | 7524 | 0.4583 | | 0.5129 | 172.0 | 7568 | 0.4674 | | 0.5302 | 173.0 | 7612 | 0.4565 | | 0.5218 | 174.0 | 7656 | 0.4552 | | 0.5189 | 175.0 | 7700 | 0.4583 | | 0.5257 | 176.0 | 7744 | 0.4529 | | 0.5216 | 177.0 | 7788 | 0.4489 | | 0.5206 | 178.0 | 7832 | 0.4460 | | 0.5241 | 179.0 | 7876 | 0.4431 | | 0.5158 | 180.0 | 7920 | 0.4413 | | 0.509 | 181.0 | 7964 | 0.4492 | | 0.5111 | 182.0 | 8008 | 0.4431 | | 0.5174 | 183.0 | 8052 | 0.4382 | | 0.5122 | 184.0 | 8096 | 0.4305 | | 0.4983 | 185.0 | 8140 | 0.4346 | | 0.5022 | 186.0 | 8184 | 0.4358 | | 0.4951 | 187.0 | 8228 | 0.4357 | | 0.4989 | 188.0 | 8272 | 0.4325 | | 0.5096 | 189.0 | 8316 | 0.4291 | | 0.503 | 190.0 | 8360 | 0.4324 | | 0.4954 | 191.0 | 8404 | 0.4277 | | 0.5071 | 192.0 | 8448 | 0.4210 | | 0.505 | 193.0 | 8492 | 0.4269 | | 0.5002 | 194.0 | 8536 | 0.4292 | | 0.5039 | 195.0 | 8580 | 0.4219 | | 0.5107 | 196.0 | 8624 | 0.4191 | | 0.5008 | 197.0 | 8668 | 0.4230 | | 0.5024 | 198.0 | 8712 | 0.4226 | | 0.4851 | 199.0 | 8756 | 0.4196 | | 0.5026 | 200.0 | 8800 | 0.4164 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.1.0+cu121 - Datasets 2.14.5 - Tokenizers 0.14.1