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bart-large-cnn-finetuned-promt_generation

Text2Text Generation
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PyTorch
bart
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bart-large-cnn-finetuned-promt_generation / README.md
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metadata
license: mit
base_model: facebook/bart-large-cnn
tags:
  - generated_from_trainer
model-index:
  - name: bart-large-cnn-finetuned-promt_generation
    results: []

bart-large-cnn-finetuned-promt_generation

This model is a fine-tuned version of facebook/bart-large-cnn on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 1.8767
  • Map: 0.3718
  • Ndcg@10: 0.5915

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 3.3856 0.2563 0.4531
No log 2.0 8 3.3740 0.2563 0.4531
No log 3.0 12 3.3430 0.2563 0.4531
No log 4.0 16 3.2912 0.2563 0.4531
No log 5.0 20 3.2468 0.2563 0.4531
No log 6.0 24 3.2199 0.2563 0.4531
No log 7.0 28 3.2016 0.2563 0.4531
No log 8.0 32 3.0741 0.2563 0.4531
No log 9.0 36 3.0260 0.2563 0.4531
No log 10.0 40 2.9989 0.2563 0.4531
No log 11.0 44 2.9755 0.2563 0.4531
No log 12.0 48 2.9495 0.2560 0.4528
No log 13.0 52 2.9300 0.2560 0.4528
No log 14.0 56 2.9088 0.2560 0.4528
No log 15.0 60 2.8656 0.2560 0.4528
No log 16.0 64 2.8146 0.2560 0.4528
No log 17.0 68 2.7699 0.2560 0.4528
No log 18.0 72 2.7321 0.2577 0.4542
No log 19.0 76 2.6978 0.2577 0.4542
No log 20.0 80 2.6665 0.2577 0.4542
No log 21.0 84 2.6373 0.2577 0.4542
No log 22.0 88 2.6080 0.2897 0.4974
No log 23.0 92 2.5812 0.2897 0.4974
No log 24.0 96 2.5568 0.2954 0.5014
No log 25.0 100 2.5348 0.2954 0.5014
No log 26.0 104 2.5133 0.2954 0.5014
No log 27.0 108 2.4929 0.2954 0.5014
No log 28.0 112 2.4735 0.3385 0.5472
No log 29.0 116 2.4553 0.3385 0.5472
No log 30.0 120 2.4374 0.3385 0.5472
No log 31.0 124 2.4201 0.3385 0.5472
No log 32.0 128 2.4035 0.3385 0.5472
No log 33.0 132 2.3870 0.3385 0.5472
No log 34.0 136 2.3711 0.3385 0.5472
No log 35.0 140 2.3556 0.3385 0.5472
No log 36.0 144 2.3397 0.3385 0.5472
No log 37.0 148 2.3246 0.3385 0.5472
No log 38.0 152 2.3097 0.3385 0.5472
No log 39.0 156 2.2944 0.3718 0.5915
No log 40.0 160 2.2801 0.3718 0.5915
No log 41.0 164 2.2660 0.3718 0.5915
No log 42.0 168 2.2525 0.3718 0.5915
No log 43.0 172 2.2392 0.3718 0.5915
No log 44.0 176 2.2267 0.3718 0.5915
No log 45.0 180 2.2135 0.3718 0.5915
No log 46.0 184 2.2007 0.3718 0.5915
No log 47.0 188 2.1875 0.3718 0.5915
No log 48.0 192 2.1752 0.3718 0.5915
No log 49.0 196 2.1637 0.3718 0.5915
No log 50.0 200 2.1514 0.3718 0.5915
No log 51.0 204 2.1393 0.3718 0.5915
No log 52.0 208 2.1281 0.3718 0.5915
No log 53.0 212 2.1159 0.3718 0.5915
No log 54.0 216 2.1048 0.3718 0.5915
No log 55.0 220 2.0941 0.3718 0.5915
No log 56.0 224 2.0829 0.3718 0.5915
No log 57.0 228 2.0727 0.3718 0.5915
No log 58.0 232 2.0617 0.3718 0.5915
No log 59.0 236 2.0518 0.3718 0.5915
No log 60.0 240 2.0416 0.3718 0.5915
No log 61.0 244 2.0323 0.3718 0.5915
No log 62.0 248 2.0230 0.3718 0.5915
No log 63.0 252 2.0143 0.3718 0.5915
No log 64.0 256 2.0060 0.3718 0.5915
No log 65.0 260 1.9977 0.3718 0.5915
No log 66.0 264 1.9901 0.3718 0.5915
No log 67.0 268 1.9827 0.3718 0.5915
No log 68.0 272 1.9757 0.3718 0.5915
No log 69.0 276 1.9690 0.3718 0.5915
No log 70.0 280 1.9622 0.3718 0.5915
No log 71.0 284 1.9561 0.3718 0.5915
No log 72.0 288 1.9505 0.3718 0.5915
No log 73.0 292 1.9447 0.3718 0.5915
No log 74.0 296 1.9401 0.3718 0.5915
No log 75.0 300 1.9349 0.3863 0.5987
No log 76.0 304 1.9303 0.3863 0.5987
No log 77.0 308 1.9254 0.3863 0.5987
No log 78.0 312 1.9209 0.3863 0.5987
No log 79.0 316 1.9171 0.3863 0.5987
No log 80.0 320 1.9133 0.3863 0.5987
No log 81.0 324 1.9098 0.3863 0.5987
No log 82.0 328 1.9067 0.3718 0.5915
No log 83.0 332 1.9034 0.3718 0.5915
No log 84.0 336 1.8999 0.3718 0.5915
No log 85.0 340 1.8975 0.3718 0.5915
No log 86.0 344 1.8949 0.3718 0.5915
No log 87.0 348 1.8928 0.3718 0.5915
No log 88.0 352 1.8902 0.3718 0.5915
No log 89.0 356 1.8880 0.3718 0.5915
No log 90.0 360 1.8859 0.3718 0.5915
No log 91.0 364 1.8845 0.3718 0.5915
No log 92.0 368 1.8829 0.3718 0.5915
No log 93.0 372 1.8819 0.3718 0.5915
No log 94.0 376 1.8803 0.3718 0.5915
No log 95.0 380 1.8801 0.3718 0.5915
No log 96.0 384 1.8782 0.3718 0.5915
No log 97.0 388 1.8782 0.3718 0.5915
No log 98.0 392 1.8773 0.3718 0.5915
No log 99.0 396 1.8773 0.3718 0.5915
No log 100.0 400 1.8767 0.3718 0.5915

Framework versions

  • Transformers 4.34.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.14.1