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mpnet-base-airlines-news-multi-label

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metadata
base_model: microsoft/mpnet-base
tags:
  - generated_from_trainer
metrics:
  - f1
model-index:
  - name: mpnet-base-airlines-news-multi-label
    results: []

mpnet-base-airlines-news-multi-label

This model is a fine-tuned version of microsoft/mpnet-base on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2601
  • F1: 0.8921
  • Roc Auc: 0.6253

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: 7e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 65

Training results

Training Loss Epoch Step Validation Loss F1 Roc Auc
No log 1.0 57 0.3852 0.8161 0.5
No log 2.0 114 0.3612 0.8161 0.5
No log 3.0 171 0.3569 0.8161 0.5
No log 4.0 228 0.3515 0.8161 0.5
No log 5.0 285 0.3453 0.8161 0.5
No log 6.0 342 0.3403 0.8161 0.5
No log 7.0 399 0.3345 0.8161 0.5
No log 8.0 456 0.3292 0.8161 0.5
0.3585 9.0 513 0.3252 0.8161 0.5
0.3585 10.0 570 0.3175 0.8161 0.5
0.3585 11.0 627 0.3129 0.8161 0.5
0.3585 12.0 684 0.3076 0.8351 0.5029
0.3585 13.0 741 0.3024 0.8425 0.5109
0.3585 14.0 798 0.2995 0.8516 0.5163
0.3585 15.0 855 0.2953 0.8528 0.5221
0.3585 16.0 912 0.2904 0.8744 0.5426
0.3585 17.0 969 0.2875 0.8738 0.5451
0.2943 18.0 1026 0.2835 0.8833 0.5798
0.2943 19.0 1083 0.2811 0.8799 0.5710
0.2943 20.0 1140 0.2786 0.8815 0.5873
0.2943 21.0 1197 0.2761 0.8815 0.5873
0.2943 22.0 1254 0.2750 0.8838 0.5906
0.2943 23.0 1311 0.2705 0.8905 0.6194
0.2943 24.0 1368 0.2687 0.8911 0.6224
0.2943 25.0 1425 0.2674 0.8895 0.6165
0.2943 26.0 1482 0.2652 0.8911 0.6224
0.2666 27.0 1539 0.2642 0.8911 0.6224
0.2666 28.0 1596 0.2634 0.8903 0.6194
0.2666 29.0 1653 0.2612 0.8903 0.6194
0.2666 30.0 1710 0.2601 0.8921 0.6253
0.2666 31.0 1767 0.2583 0.8913 0.6328
0.2666 32.0 1824 0.2568 0.8864 0.6319
0.2666 33.0 1881 0.2563 0.8861 0.6319
0.2666 34.0 1938 0.2552 0.8869 0.6349
0.2666 35.0 1995 0.2544 0.8884 0.6378
0.2516 36.0 2052 0.2530 0.8875 0.6374
0.2516 37.0 2109 0.2523 0.8876 0.6374
0.2516 38.0 2166 0.2514 0.8889 0.6432
0.2516 39.0 2223 0.2504 0.8874 0.6453
0.2516 40.0 2280 0.2502 0.8892 0.6432
0.2516 41.0 2337 0.2495 0.8862 0.6419
0.2516 42.0 2394 0.2490 0.8867 0.6445
0.2516 43.0 2451 0.2491 0.8859 0.6365
0.2442 44.0 2508 0.2480 0.8906 0.6511
0.2442 45.0 2565 0.2476 0.8894 0.6457
0.2442 46.0 2622 0.2476 0.8888 0.6478
0.2442 47.0 2679 0.2474 0.8906 0.6511
0.2442 48.0 2736 0.2462 0.8890 0.6507
0.2442 49.0 2793 0.2461 0.8920 0.6545
0.2442 50.0 2850 0.2455 0.8894 0.6532
0.2442 51.0 2907 0.2457 0.8897 0.6507
0.2442 52.0 2964 0.2452 0.8894 0.6532
0.238 53.0 3021 0.2449 0.8903 0.6536
0.238 54.0 3078 0.2447 0.8894 0.6532
0.238 55.0 3135 0.2446 0.8894 0.6532
0.238 56.0 3192 0.2446 0.8904 0.6536
0.238 57.0 3249 0.2443 0.8894 0.6532
0.238 58.0 3306 0.2441 0.8894 0.6532
0.238 59.0 3363 0.2440 0.8911 0.6566
0.238 60.0 3420 0.2440 0.8911 0.6566
0.238 61.0 3477 0.2439 0.8903 0.6536
0.2353 62.0 3534 0.2437 0.8911 0.6566
0.2353 63.0 3591 0.2438 0.8911 0.6566
0.2353 64.0 3648 0.2437 0.8911 0.6566
0.2353 65.0 3705 0.2437 0.8911 0.6566

Framework versions

  • Transformers 4.41.1
  • Pytorch 2.3.0+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.1