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--- |
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license: mit |
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base_model: facebook/bart-large-cnn |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: bart-large-cnn-finetuned-promt_generation |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# bart-large-cnn-finetuned-promt_generation |
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This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.8767 |
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- Map: 0.3718 |
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- Ndcg@10: 0.5915 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-07 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 100 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Map | Ndcg@10 | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| |
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| No log | 1.0 | 4 | 3.3856 | 0.2563 | 0.4531 | |
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| No log | 2.0 | 8 | 3.3740 | 0.2563 | 0.4531 | |
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| No log | 3.0 | 12 | 3.3430 | 0.2563 | 0.4531 | |
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| No log | 4.0 | 16 | 3.2912 | 0.2563 | 0.4531 | |
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| No log | 5.0 | 20 | 3.2468 | 0.2563 | 0.4531 | |
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| No log | 6.0 | 24 | 3.2199 | 0.2563 | 0.4531 | |
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| No log | 7.0 | 28 | 3.2016 | 0.2563 | 0.4531 | |
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| No log | 8.0 | 32 | 3.0741 | 0.2563 | 0.4531 | |
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| No log | 9.0 | 36 | 3.0260 | 0.2563 | 0.4531 | |
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| No log | 10.0 | 40 | 2.9989 | 0.2563 | 0.4531 | |
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| No log | 11.0 | 44 | 2.9755 | 0.2563 | 0.4531 | |
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| No log | 12.0 | 48 | 2.9495 | 0.2560 | 0.4528 | |
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| No log | 13.0 | 52 | 2.9300 | 0.2560 | 0.4528 | |
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| No log | 14.0 | 56 | 2.9088 | 0.2560 | 0.4528 | |
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| No log | 15.0 | 60 | 2.8656 | 0.2560 | 0.4528 | |
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| No log | 16.0 | 64 | 2.8146 | 0.2560 | 0.4528 | |
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| No log | 17.0 | 68 | 2.7699 | 0.2560 | 0.4528 | |
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| No log | 18.0 | 72 | 2.7321 | 0.2577 | 0.4542 | |
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| No log | 19.0 | 76 | 2.6978 | 0.2577 | 0.4542 | |
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| No log | 20.0 | 80 | 2.6665 | 0.2577 | 0.4542 | |
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| No log | 21.0 | 84 | 2.6373 | 0.2577 | 0.4542 | |
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| No log | 22.0 | 88 | 2.6080 | 0.2897 | 0.4974 | |
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| No log | 23.0 | 92 | 2.5812 | 0.2897 | 0.4974 | |
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| No log | 24.0 | 96 | 2.5568 | 0.2954 | 0.5014 | |
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| No log | 25.0 | 100 | 2.5348 | 0.2954 | 0.5014 | |
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| No log | 26.0 | 104 | 2.5133 | 0.2954 | 0.5014 | |
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| No log | 27.0 | 108 | 2.4929 | 0.2954 | 0.5014 | |
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| No log | 28.0 | 112 | 2.4735 | 0.3385 | 0.5472 | |
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| No log | 29.0 | 116 | 2.4553 | 0.3385 | 0.5472 | |
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| No log | 30.0 | 120 | 2.4374 | 0.3385 | 0.5472 | |
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| No log | 31.0 | 124 | 2.4201 | 0.3385 | 0.5472 | |
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| No log | 32.0 | 128 | 2.4035 | 0.3385 | 0.5472 | |
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| No log | 33.0 | 132 | 2.3870 | 0.3385 | 0.5472 | |
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| No log | 34.0 | 136 | 2.3711 | 0.3385 | 0.5472 | |
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| No log | 35.0 | 140 | 2.3556 | 0.3385 | 0.5472 | |
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| No log | 36.0 | 144 | 2.3397 | 0.3385 | 0.5472 | |
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| No log | 37.0 | 148 | 2.3246 | 0.3385 | 0.5472 | |
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| No log | 38.0 | 152 | 2.3097 | 0.3385 | 0.5472 | |
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| No log | 39.0 | 156 | 2.2944 | 0.3718 | 0.5915 | |
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| No log | 40.0 | 160 | 2.2801 | 0.3718 | 0.5915 | |
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| No log | 41.0 | 164 | 2.2660 | 0.3718 | 0.5915 | |
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| No log | 42.0 | 168 | 2.2525 | 0.3718 | 0.5915 | |
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| No log | 43.0 | 172 | 2.2392 | 0.3718 | 0.5915 | |
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| No log | 44.0 | 176 | 2.2267 | 0.3718 | 0.5915 | |
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| No log | 45.0 | 180 | 2.2135 | 0.3718 | 0.5915 | |
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| No log | 46.0 | 184 | 2.2007 | 0.3718 | 0.5915 | |
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| No log | 47.0 | 188 | 2.1875 | 0.3718 | 0.5915 | |
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| No log | 48.0 | 192 | 2.1752 | 0.3718 | 0.5915 | |
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| No log | 49.0 | 196 | 2.1637 | 0.3718 | 0.5915 | |
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| No log | 50.0 | 200 | 2.1514 | 0.3718 | 0.5915 | |
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| No log | 51.0 | 204 | 2.1393 | 0.3718 | 0.5915 | |
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| No log | 52.0 | 208 | 2.1281 | 0.3718 | 0.5915 | |
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| No log | 53.0 | 212 | 2.1159 | 0.3718 | 0.5915 | |
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| No log | 54.0 | 216 | 2.1048 | 0.3718 | 0.5915 | |
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| No log | 55.0 | 220 | 2.0941 | 0.3718 | 0.5915 | |
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| No log | 56.0 | 224 | 2.0829 | 0.3718 | 0.5915 | |
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| No log | 57.0 | 228 | 2.0727 | 0.3718 | 0.5915 | |
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| No log | 58.0 | 232 | 2.0617 | 0.3718 | 0.5915 | |
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| No log | 59.0 | 236 | 2.0518 | 0.3718 | 0.5915 | |
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| No log | 60.0 | 240 | 2.0416 | 0.3718 | 0.5915 | |
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| No log | 61.0 | 244 | 2.0323 | 0.3718 | 0.5915 | |
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| No log | 62.0 | 248 | 2.0230 | 0.3718 | 0.5915 | |
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| No log | 63.0 | 252 | 2.0143 | 0.3718 | 0.5915 | |
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| No log | 64.0 | 256 | 2.0060 | 0.3718 | 0.5915 | |
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| No log | 65.0 | 260 | 1.9977 | 0.3718 | 0.5915 | |
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| No log | 66.0 | 264 | 1.9901 | 0.3718 | 0.5915 | |
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| No log | 67.0 | 268 | 1.9827 | 0.3718 | 0.5915 | |
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| No log | 68.0 | 272 | 1.9757 | 0.3718 | 0.5915 | |
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| No log | 69.0 | 276 | 1.9690 | 0.3718 | 0.5915 | |
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| No log | 70.0 | 280 | 1.9622 | 0.3718 | 0.5915 | |
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| No log | 71.0 | 284 | 1.9561 | 0.3718 | 0.5915 | |
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| No log | 72.0 | 288 | 1.9505 | 0.3718 | 0.5915 | |
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| No log | 73.0 | 292 | 1.9447 | 0.3718 | 0.5915 | |
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| No log | 74.0 | 296 | 1.9401 | 0.3718 | 0.5915 | |
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| No log | 75.0 | 300 | 1.9349 | 0.3863 | 0.5987 | |
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| No log | 76.0 | 304 | 1.9303 | 0.3863 | 0.5987 | |
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| No log | 77.0 | 308 | 1.9254 | 0.3863 | 0.5987 | |
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| No log | 78.0 | 312 | 1.9209 | 0.3863 | 0.5987 | |
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| No log | 79.0 | 316 | 1.9171 | 0.3863 | 0.5987 | |
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| No log | 80.0 | 320 | 1.9133 | 0.3863 | 0.5987 | |
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| No log | 81.0 | 324 | 1.9098 | 0.3863 | 0.5987 | |
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| No log | 82.0 | 328 | 1.9067 | 0.3718 | 0.5915 | |
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| No log | 83.0 | 332 | 1.9034 | 0.3718 | 0.5915 | |
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| No log | 84.0 | 336 | 1.8999 | 0.3718 | 0.5915 | |
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| No log | 85.0 | 340 | 1.8975 | 0.3718 | 0.5915 | |
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| No log | 86.0 | 344 | 1.8949 | 0.3718 | 0.5915 | |
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| No log | 87.0 | 348 | 1.8928 | 0.3718 | 0.5915 | |
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| No log | 88.0 | 352 | 1.8902 | 0.3718 | 0.5915 | |
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| No log | 89.0 | 356 | 1.8880 | 0.3718 | 0.5915 | |
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| No log | 90.0 | 360 | 1.8859 | 0.3718 | 0.5915 | |
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| No log | 91.0 | 364 | 1.8845 | 0.3718 | 0.5915 | |
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| No log | 92.0 | 368 | 1.8829 | 0.3718 | 0.5915 | |
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| No log | 93.0 | 372 | 1.8819 | 0.3718 | 0.5915 | |
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| No log | 94.0 | 376 | 1.8803 | 0.3718 | 0.5915 | |
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| No log | 95.0 | 380 | 1.8801 | 0.3718 | 0.5915 | |
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| No log | 96.0 | 384 | 1.8782 | 0.3718 | 0.5915 | |
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| No log | 97.0 | 388 | 1.8782 | 0.3718 | 0.5915 | |
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| No log | 98.0 | 392 | 1.8773 | 0.3718 | 0.5915 | |
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| No log | 99.0 | 396 | 1.8773 | 0.3718 | 0.5915 | |
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| No log | 100.0 | 400 | 1.8767 | 0.3718 | 0.5915 | |
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### Framework versions |
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- Transformers 4.34.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.1 |
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