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bart-large-cnn-finetuned-prompt_generation

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metadata
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 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