--- 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 the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8970 - Map: 0.7119 - Ndcg@10: 0.8169 ## 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: 8 - eval_batch_size: 8 - 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 | Map | Ndcg@10 | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | No log | 1.0 | 4 | 3.0497 | 0.2577 | 0.4542 | | No log | 2.0 | 8 | 3.0207 | 0.2563 | 0.4531 | | No log | 3.0 | 12 | 2.9998 | 0.2563 | 0.4531 | | No log | 4.0 | 16 | 2.9299 | 0.2563 | 0.4531 | | No log | 5.0 | 20 | 2.8971 | 0.2577 | 0.4542 | | No log | 6.0 | 24 | 2.8682 | 0.2577 | 0.4542 | | No log | 7.0 | 28 | 2.8340 | 0.2577 | 0.4542 | | No log | 8.0 | 32 | 2.7096 | 0.2577 | 0.4542 | | No log | 9.0 | 36 | 2.6644 | 0.2577 | 0.4542 | | No log | 10.0 | 40 | 2.6361 | 0.2577 | 0.4542 | | No log | 11.0 | 44 | 2.6026 | 0.2577 | 0.4542 | | No log | 12.0 | 48 | 2.5729 | 0.2577 | 0.4542 | | No log | 13.0 | 52 | 2.5500 | 0.2577 | 0.4542 | | No log | 14.0 | 56 | 2.5108 | 0.2577 | 0.4542 | | No log | 15.0 | 60 | 2.4473 | 0.2577 | 0.4542 | | No log | 16.0 | 64 | 2.3941 | 0.2708 | 0.4626 | | No log | 17.0 | 68 | 2.3544 | 0.3326 | 0.5333 | | No log | 18.0 | 72 | 2.3221 | 0.3326 | 0.5333 | | No log | 19.0 | 76 | 2.2970 | 0.3326 | 0.5333 | | No log | 20.0 | 80 | 2.2762 | 0.3813 | 0.5865 | | No log | 21.0 | 84 | 2.2560 | 0.3813 | 0.5865 | | No log | 22.0 | 88 | 2.2345 | 0.4076 | 0.6002 | | No log | 23.0 | 92 | 2.2151 | 0.4076 | 0.6002 | | No log | 24.0 | 96 | 2.1989 | 0.4076 | 0.6002 | | No log | 25.0 | 100 | 2.1844 | 0.4076 | 0.6002 | | No log | 26.0 | 104 | 2.1719 | 0.4076 | 0.6002 | | No log | 27.0 | 108 | 2.1597 | 0.4076 | 0.6002 | | No log | 28.0 | 112 | 2.1485 | 0.4076 | 0.6002 | | No log | 29.0 | 116 | 2.1376 | 0.4076 | 0.6002 | | No log | 30.0 | 120 | 2.1268 | 0.4076 | 0.6002 | | No log | 31.0 | 124 | 2.1169 | 0.4076 | 0.6002 | | No log | 32.0 | 128 | 2.1075 | 0.4076 | 0.6002 | | No log | 33.0 | 132 | 2.0984 | 0.4076 | 0.6002 | | No log | 34.0 | 136 | 2.0899 | 0.4751 | 0.6664 | | No log | 35.0 | 140 | 2.0816 | 0.4751 | 0.6664 | | No log | 36.0 | 144 | 2.0739 | 0.4751 | 0.6664 | | No log | 37.0 | 148 | 2.0661 | 0.4751 | 0.6664 | | No log | 38.0 | 152 | 2.0581 | 0.4751 | 0.6664 | | No log | 39.0 | 156 | 2.0504 | 0.4751 | 0.6664 | | No log | 40.0 | 160 | 2.0436 | 0.4751 | 0.6664 | | No log | 41.0 | 164 | 2.0362 | 0.5569 | 0.7126 | | No log | 42.0 | 168 | 2.0293 | 0.5569 | 0.7126 | | No log | 43.0 | 172 | 2.0226 | 0.5569 | 0.7126 | | No log | 44.0 | 176 | 2.0162 | 0.5569 | 0.7126 | | No log | 45.0 | 180 | 2.0103 | 0.6018 | 0.7455 | | No log | 46.0 | 184 | 2.0039 | 0.7313 | 0.8296 | | No log | 47.0 | 188 | 1.9983 | 0.7313 | 0.8296 | | No log | 48.0 | 192 | 1.9931 | 0.7313 | 0.8296 | | No log | 49.0 | 196 | 1.9883 | 0.7313 | 0.8296 | | No log | 50.0 | 200 | 1.9832 | 0.7313 | 0.8296 | | No log | 51.0 | 204 | 1.9787 | 0.7313 | 0.8296 | | No log | 52.0 | 208 | 1.9751 | 0.7313 | 0.8296 | | No log | 53.0 | 212 | 1.9710 | 0.7313 | 0.8296 | | No log | 54.0 | 216 | 1.9660 | 0.7313 | 0.8296 | | No log | 55.0 | 220 | 1.9627 | 0.7313 | 0.8296 | | No log | 56.0 | 224 | 1.9586 | 0.7313 | 0.8296 | | No log | 57.0 | 228 | 1.9550 | 0.7313 | 0.8296 | | No log | 58.0 | 232 | 1.9514 | 0.7313 | 0.8296 | | No log | 59.0 | 236 | 1.9480 | 0.7313 | 0.8296 | | No log | 60.0 | 240 | 1.9448 | 0.7313 | 0.8296 | | No log | 61.0 | 244 | 1.9421 | 0.7313 | 0.8296 | | No log | 62.0 | 248 | 1.9386 | 0.7313 | 0.8296 | | No log | 63.0 | 252 | 1.9358 | 0.7313 | 0.8296 | | No log | 64.0 | 256 | 1.9330 | 0.7313 | 0.8296 | | No log | 65.0 | 260 | 1.9309 | 0.7313 | 0.8296 | | No log | 66.0 | 264 | 1.9284 | 0.7313 | 0.8296 | | No log | 67.0 | 268 | 1.9266 | 0.7313 | 0.8296 | | No log | 68.0 | 272 | 1.9246 | 0.7313 | 0.8296 | | No log | 69.0 | 276 | 1.9225 | 0.7313 | 0.8296 | | No log | 70.0 | 280 | 1.9207 | 0.7313 | 0.8296 | | No log | 71.0 | 284 | 1.9195 | 0.7313 | 0.8296 | | No log | 72.0 | 288 | 1.9174 | 0.7680 | 0.8517 | | No log | 73.0 | 292 | 1.9158 | 0.7680 | 0.8517 | | No log | 74.0 | 296 | 1.9142 | 0.7680 | 0.8517 | | No log | 75.0 | 300 | 1.9124 | 0.7680 | 0.8517 | | No log | 76.0 | 304 | 1.9112 | 0.7680 | 0.8517 | | No log | 77.0 | 308 | 1.9095 | 0.7680 | 0.8517 | | No log | 78.0 | 312 | 1.9083 | 0.7680 | 0.8517 | | No log | 79.0 | 316 | 1.9071 | 0.7119 | 0.8169 | | No log | 80.0 | 320 | 1.9059 | 0.7119 | 0.8169 | | No log | 81.0 | 324 | 1.9053 | 0.7119 | 0.8169 | | No log | 82.0 | 328 | 1.9044 | 0.7119 | 0.8169 | | No log | 83.0 | 332 | 1.9035 | 0.7119 | 0.8169 | | No log | 84.0 | 336 | 1.9028 | 0.7119 | 0.8169 | | No log | 85.0 | 340 | 1.9019 | 0.7119 | 0.8169 | | No log | 86.0 | 344 | 1.9013 | 0.7119 | 0.8169 | | No log | 87.0 | 348 | 1.9006 | 0.7119 | 0.8169 | | No log | 88.0 | 352 | 1.9001 | 0.7119 | 0.8169 | | No log | 89.0 | 356 | 1.8997 | 0.7119 | 0.8169 | | No log | 90.0 | 360 | 1.8989 | 0.7119 | 0.8169 | | No log | 91.0 | 364 | 1.8989 | 0.7119 | 0.8169 | | No log | 92.0 | 368 | 1.8982 | 0.7119 | 0.8169 | | No log | 93.0 | 372 | 1.8983 | 0.7119 | 0.8169 | | No log | 94.0 | 376 | 1.8979 | 0.7119 | 0.8169 | | No log | 95.0 | 380 | 1.8980 | 0.7680 | 0.8517 | | No log | 96.0 | 384 | 1.8973 | 0.7119 | 0.8169 | | No log | 97.0 | 388 | 1.8973 | 0.7680 | 0.8517 | | No log | 98.0 | 392 | 1.8971 | 0.7119 | 0.8169 | | No log | 99.0 | 396 | 1.8968 | 0.7119 | 0.8169 | | No log | 100.0 | 400 | 1.8970 | 0.7119 | 0.8169 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.1