--- license: mit base_model: facebook/bart-large-cnn tags: - generated_from_trainer model-index: - name: bart-large-cnn-finetuned-prompt_generation results: [] --- # bart-large-cnn-finetuned-prompt_generation This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.8294 - Actual score: 0.8766 - Predction score: -0.6178 - Score difference: 1.4944 ## 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: 3e-07 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Actual score | Predction score | Score difference | |:-------------:|:-----:|:----:|:---------------:|:------------:|:---------------:|:----------------:| | No log | 1.0 | 2 | 3.6607 | 0.8766 | -0.3976 | 1.2742 | | No log | 2.0 | 4 | 3.6575 | 0.8766 | -0.4128 | 1.2894 | | No log | 3.0 | 6 | 3.6485 | 0.8766 | -0.3426 | 1.2192 | | No log | 4.0 | 8 | 3.6279 | 0.8766 | -0.4158 | 1.2924 | | No log | 5.0 | 10 | 3.6199 | 0.8766 | -0.4332 | 1.3099 | | No log | 6.0 | 12 | 3.6119 | 0.8766 | -0.2640 | 1.1406 | | No log | 7.0 | 14 | 3.6076 | 0.8766 | -0.3007 | 1.1773 | | No log | 8.0 | 16 | 3.5413 | 0.8766 | -0.2210 | 1.0976 | | No log | 9.0 | 18 | 3.5274 | 0.8766 | -0.2317 | 1.1083 | | No log | 10.0 | 20 | 3.5184 | 0.8766 | -0.2801 | 1.1567 | | No log | 11.0 | 22 | 3.5041 | 0.8766 | -0.2898 | 1.1664 | | No log | 12.0 | 24 | 3.4935 | 0.8766 | -0.3675 | 1.2441 | | No log | 13.0 | 26 | 3.4858 | 0.8766 | -0.3410 | 1.2176 | | No log | 14.0 | 28 | 3.4763 | 0.8766 | -0.1891 | 1.0658 | | No log | 15.0 | 30 | 3.3761 | 0.8766 | -0.3789 | 1.2556 | | No log | 16.0 | 32 | 3.3314 | 0.8766 | -0.2348 | 1.1114 | | No log | 17.0 | 34 | 3.3103 | 0.8766 | -0.2213 | 1.0979 | | No log | 18.0 | 36 | 3.2951 | 0.8766 | -0.2949 | 1.1715 | | No log | 19.0 | 38 | 3.2811 | 0.8766 | -0.3811 | 1.2577 | | No log | 20.0 | 40 | 3.2708 | 0.8766 | -0.3883 | 1.2649 | | No log | 21.0 | 42 | 3.2625 | 0.8766 | -0.4219 | 1.2986 | | No log | 22.0 | 44 | 3.2471 | 0.8766 | -0.2971 | 1.1737 | | No log | 23.0 | 46 | 3.2308 | 0.8766 | -0.1368 | 1.0134 | | No log | 24.0 | 48 | 3.2171 | 0.8766 | -0.1705 | 1.0471 | | No log | 25.0 | 50 | 3.2068 | 0.8766 | -0.2057 | 1.0823 | | No log | 26.0 | 52 | 3.1972 | 0.8766 | -0.1984 | 1.0750 | | No log | 27.0 | 54 | 3.1892 | 0.8766 | -0.4348 | 1.3114 | | No log | 28.0 | 56 | 3.1812 | 0.8766 | -0.4045 | 1.2811 | | No log | 29.0 | 58 | 3.1681 | 0.8766 | -0.3908 | 1.2675 | | No log | 30.0 | 60 | 3.1422 | 0.8766 | -0.4513 | 1.3279 | | No log | 31.0 | 62 | 3.1154 | 0.8766 | -0.4580 | 1.3346 | | No log | 32.0 | 64 | 3.0906 | 0.8766 | -0.4082 | 1.2848 | | No log | 33.0 | 66 | 3.0680 | 0.8766 | -0.4836 | 1.3602 | | No log | 34.0 | 68 | 3.0476 | 0.8766 | -0.4555 | 1.3321 | | No log | 35.0 | 70 | 3.0301 | 0.8766 | -0.5186 | 1.3952 | | No log | 36.0 | 72 | 3.0159 | 0.8766 | -0.4299 | 1.3065 | | No log | 37.0 | 74 | 3.0040 | 0.8766 | -0.4216 | 1.2982 | | No log | 38.0 | 76 | 2.9937 | 0.8766 | -0.5763 | 1.4530 | | No log | 39.0 | 78 | 2.9842 | 0.8766 | -0.6791 | 1.5557 | | No log | 40.0 | 80 | 2.9759 | 0.8766 | -0.6260 | 1.5026 | | No log | 41.0 | 82 | 2.9686 | 0.8766 | -0.6331 | 1.5097 | | No log | 42.0 | 84 | 2.9622 | 0.8766 | -0.5588 | 1.4354 | | No log | 43.0 | 86 | 2.9565 | 0.8766 | -0.5719 | 1.4485 | | No log | 44.0 | 88 | 2.9512 | 0.8766 | -0.5433 | 1.4199 | | No log | 45.0 | 90 | 2.9462 | 0.8766 | -0.5528 | 1.4294 | | No log | 46.0 | 92 | 2.9416 | 0.8766 | -0.5487 | 1.4253 | | No log | 47.0 | 94 | 2.9372 | 0.8766 | -0.5130 | 1.3896 | | No log | 48.0 | 96 | 2.9325 | 0.8766 | -0.5495 | 1.4262 | | No log | 49.0 | 98 | 2.9278 | 0.8766 | -0.5334 | 1.4101 | | No log | 50.0 | 100 | 2.9228 | 0.8766 | -0.5954 | 1.4720 | | No log | 51.0 | 102 | 2.9178 | 0.8766 | -0.5583 | 1.4349 | | No log | 52.0 | 104 | 2.9127 | 0.8766 | -0.4640 | 1.3406 | | No log | 53.0 | 106 | 2.9081 | 0.8766 | -0.4567 | 1.3333 | | No log | 54.0 | 108 | 2.9037 | 0.8766 | -0.4877 | 1.3643 | | No log | 55.0 | 110 | 2.8995 | 0.8766 | -0.4779 | 1.3546 | | No log | 56.0 | 112 | 2.8957 | 0.8766 | -0.4815 | 1.3581 | | No log | 57.0 | 114 | 2.8922 | 0.8766 | -0.4051 | 1.2817 | | No log | 58.0 | 116 | 2.8886 | 0.8766 | -0.4100 | 1.2866 | | No log | 59.0 | 118 | 2.8854 | 0.8766 | -0.4069 | 1.2835 | | No log | 60.0 | 120 | 2.8822 | 0.8766 | -0.4390 | 1.3156 | | No log | 61.0 | 122 | 2.8793 | 0.8766 | -0.4077 | 1.2844 | | No log | 62.0 | 124 | 2.8766 | 0.8766 | -0.4278 | 1.3045 | | No log | 63.0 | 126 | 2.8738 | 0.8766 | -0.4430 | 1.3196 | | No log | 64.0 | 128 | 2.8712 | 0.8766 | -0.4711 | 1.3477 | | No log | 65.0 | 130 | 2.8688 | 0.8766 | -0.4294 | 1.3061 | | No log | 66.0 | 132 | 2.8665 | 0.8766 | -0.4669 | 1.3435 | | No log | 67.0 | 134 | 2.8642 | 0.8766 | -0.4831 | 1.3597 | | No log | 68.0 | 136 | 2.8620 | 0.8766 | -0.5078 | 1.3844 | | No log | 69.0 | 138 | 2.8599 | 0.8766 | -0.4924 | 1.3691 | | No log | 70.0 | 140 | 2.8580 | 0.8766 | -0.5569 | 1.4336 | | No log | 71.0 | 142 | 2.8560 | 0.8766 | -0.6560 | 1.5327 | | No log | 72.0 | 144 | 2.8542 | 0.8766 | -0.6354 | 1.5120 | | No log | 73.0 | 146 | 2.8525 | 0.8766 | -0.6496 | 1.5262 | | No log | 74.0 | 148 | 2.8508 | 0.8766 | -0.6530 | 1.5296 | | No log | 75.0 | 150 | 2.8491 | 0.8766 | -0.6868 | 1.5634 | | No log | 76.0 | 152 | 2.8476 | 0.8766 | -0.6260 | 1.5026 | | No log | 77.0 | 154 | 2.8460 | 0.8766 | -0.6303 | 1.5069 | | No log | 78.0 | 156 | 2.8447 | 0.8766 | -0.6137 | 1.4903 | | No log | 79.0 | 158 | 2.8433 | 0.8766 | -0.5980 | 1.4746 | | No log | 80.0 | 160 | 2.8420 | 0.8766 | -0.5799 | 1.4565 | | No log | 81.0 | 162 | 2.8409 | 0.8766 | -0.6208 | 1.4975 | | No log | 82.0 | 164 | 2.8397 | 0.8766 | -0.6227 | 1.4993 | | No log | 83.0 | 166 | 2.8387 | 0.8766 | -0.6545 | 1.5311 | | No log | 84.0 | 168 | 2.8377 | 0.8766 | -0.6560 | 1.5327 | | No log | 85.0 | 170 | 2.8366 | 0.8766 | -0.6943 | 1.5709 | | No log | 86.0 | 172 | 2.8357 | 0.8766 | -0.6259 | 1.5025 | | No log | 87.0 | 174 | 2.8350 | 0.8766 | -0.6605 | 1.5371 | | No log | 88.0 | 176 | 2.8342 | 0.8766 | -0.6590 | 1.5356 | | No log | 89.0 | 178 | 2.8334 | 0.8766 | -0.6557 | 1.5324 | | No log | 90.0 | 180 | 2.8328 | 0.8766 | -0.6482 | 1.5249 | | No log | 91.0 | 182 | 2.8321 | 0.8766 | -0.6397 | 1.5163 | | No log | 92.0 | 184 | 2.8316 | 0.8766 | -0.6501 | 1.5267 | | No log | 93.0 | 186 | 2.8311 | 0.8766 | -0.6567 | 1.5333 | | No log | 94.0 | 188 | 2.8308 | 0.8766 | -0.6441 | 1.5207 | | No log | 95.0 | 190 | 2.8304 | 0.8766 | -0.6463 | 1.5229 | | No log | 96.0 | 192 | 2.8302 | 0.8766 | -0.6614 | 1.5380 | | No log | 97.0 | 194 | 2.8300 | 0.8766 | -0.6041 | 1.4807 | | No log | 98.0 | 196 | 2.8297 | 0.8766 | -0.6222 | 1.4988 | | No log | 99.0 | 198 | 2.8295 | 0.8766 | -0.6509 | 1.5276 | | No log | 100.0 | 200 | 2.8294 | 0.8766 | -0.6178 | 1.4944 | ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1