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scenario-non-kd-scr-ner-half-mdeberta_data-univner_full55

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deberta-v2
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metadata
library_name: transformers
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: scenario-non-kd-scr-ner-half-mdeberta_data-univner_full55
    results: []

scenario-non-kd-scr-ner-half-mdeberta_data-univner_full55

This model is a fine-tuned version of microsoft/mdeberta-v3-base on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3355
  • Precision: 0.6133
  • Recall: 0.5878
  • F1: 0.6003
  • Accuracy: 0.9612

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-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 55
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 30

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
0.3541 0.2910 500 0.2833 0.3113 0.1163 0.1693 0.9287
0.2371 0.5821 1000 0.2120 0.3611 0.2493 0.2950 0.9382
0.1767 0.8731 1500 0.1741 0.4010 0.3865 0.3936 0.9464
0.1351 1.1641 2000 0.1657 0.4667 0.4367 0.4512 0.9511
0.1102 1.4552 2500 0.1532 0.4867 0.5260 0.5056 0.9542
0.1002 1.7462 3000 0.1520 0.5199 0.5269 0.5234 0.9565
0.0899 2.0373 3500 0.1584 0.5512 0.5116 0.5307 0.9574
0.0681 2.3283 4000 0.1583 0.5564 0.5308 0.5433 0.9583
0.0647 2.6193 4500 0.1532 0.5687 0.5555 0.5620 0.9595
0.0637 2.9104 5000 0.1519 0.5798 0.5726 0.5762 0.9605
0.05 3.2014 5500 0.1600 0.5696 0.5904 0.5798 0.9596
0.0449 3.4924 6000 0.1660 0.5790 0.5754 0.5772 0.9602
0.0437 3.7835 6500 0.1589 0.5820 0.5885 0.5853 0.9610
0.0396 4.0745 7000 0.1690 0.5779 0.5926 0.5851 0.9608
0.0293 4.3655 7500 0.1803 0.5815 0.5845 0.5830 0.9609
0.0325 4.6566 8000 0.1726 0.5927 0.5953 0.5940 0.9615
0.0314 4.9476 8500 0.1743 0.5756 0.6148 0.5945 0.9604
0.0215 5.2386 9000 0.1860 0.5725 0.6006 0.5863 0.9604
0.0205 5.5297 9500 0.1973 0.5849 0.5838 0.5843 0.9604
0.0223 5.8207 10000 0.1943 0.6066 0.5917 0.5990 0.9612
0.0205 6.1118 10500 0.2040 0.6086 0.5868 0.5975 0.9611
0.0155 6.4028 11000 0.2090 0.5869 0.5963 0.5916 0.9607
0.0163 6.6938 11500 0.2104 0.5972 0.5874 0.5922 0.9610
0.0168 6.9849 12000 0.2088 0.5784 0.5976 0.5879 0.9603
0.0106 7.2759 12500 0.2262 0.5997 0.5872 0.5934 0.9605
0.0114 7.5669 13000 0.2251 0.6102 0.5842 0.5969 0.9616
0.0122 7.8580 13500 0.2244 0.5989 0.5940 0.5965 0.9606
0.0111 8.1490 14000 0.2333 0.5996 0.5825 0.5909 0.9612
0.0086 8.4400 14500 0.2320 0.5881 0.5960 0.5920 0.9603
0.0089 8.7311 15000 0.2440 0.6076 0.5852 0.5962 0.9610
0.0087 9.0221 15500 0.2407 0.5978 0.5897 0.5937 0.9612
0.0065 9.3132 16000 0.2479 0.6046 0.5827 0.5934 0.9613
0.0074 9.6042 16500 0.2458 0.6007 0.5864 0.5934 0.9609
0.0076 9.8952 17000 0.2495 0.5944 0.5920 0.5932 0.9608
0.0055 10.1863 17500 0.2517 0.6030 0.5933 0.5981 0.9610
0.0048 10.4773 18000 0.2694 0.5975 0.5778 0.5875 0.9602
0.0049 10.7683 18500 0.2642 0.6110 0.5882 0.5994 0.9608
0.0061 11.0594 19000 0.2776 0.6150 0.5651 0.5890 0.9612
0.0037 11.3504 19500 0.2723 0.6132 0.5842 0.5983 0.9613
0.0045 11.6414 20000 0.2687 0.6065 0.5832 0.5946 0.9607
0.0047 11.9325 20500 0.2776 0.6153 0.5673 0.5903 0.9610
0.004 12.2235 21000 0.2806 0.6030 0.5763 0.5893 0.9612
0.0033 12.5146 21500 0.2838 0.6173 0.5791 0.5976 0.9617
0.0038 12.8056 22000 0.2884 0.6175 0.5705 0.5931 0.9611
0.0034 13.0966 22500 0.2863 0.6082 0.5843 0.5960 0.9611
0.0023 13.3877 23000 0.2905 0.6222 0.5806 0.6007 0.9618
0.003 13.6787 23500 0.2897 0.6094 0.5885 0.5988 0.9612
0.0034 13.9697 24000 0.2909 0.6126 0.5820 0.5969 0.9611
0.0021 14.2608 24500 0.2951 0.5846 0.6029 0.5936 0.9604
0.0028 14.5518 25000 0.2899 0.6086 0.5913 0.5998 0.9612
0.0025 14.8428 25500 0.3014 0.6205 0.5719 0.5952 0.9610
0.0024 15.1339 26000 0.3018 0.6173 0.5745 0.5951 0.9610
0.0019 15.4249 26500 0.3058 0.6235 0.5738 0.5976 0.9614
0.0021 15.7159 27000 0.3053 0.6220 0.5868 0.6039 0.9613
0.0019 16.0070 27500 0.3142 0.6098 0.5689 0.5886 0.9608
0.0018 16.2980 28000 0.2999 0.6057 0.5985 0.6021 0.9615
0.0017 16.5891 28500 0.3096 0.6015 0.5822 0.5917 0.9605
0.0017 16.8801 29000 0.3091 0.6159 0.5840 0.5995 0.9613
0.0023 17.1711 29500 0.3051 0.6161 0.5913 0.6034 0.9615
0.0012 17.4622 30000 0.3167 0.6283 0.5722 0.5990 0.9612
0.0012 17.7532 30500 0.3246 0.6197 0.5682 0.5928 0.9612
0.002 18.0442 31000 0.3197 0.6020 0.5887 0.5953 0.9608
0.0013 18.3353 31500 0.3146 0.6031 0.5923 0.5977 0.9610
0.0015 18.6263 32000 0.3228 0.6096 0.5827 0.5959 0.9612
0.0011 18.9173 32500 0.3248 0.6178 0.5731 0.5946 0.9611
0.0011 19.2084 33000 0.3195 0.6125 0.5904 0.6012 0.9611
0.0011 19.4994 33500 0.3340 0.6205 0.5646 0.5912 0.9613
0.0012 19.7905 34000 0.3270 0.6077 0.5839 0.5956 0.9612
0.0012 20.0815 34500 0.3231 0.6135 0.5928 0.6030 0.9612
0.0012 20.3725 35000 0.3282 0.6126 0.5803 0.5960 0.9612
0.001 20.6636 35500 0.3340 0.5999 0.5851 0.5924 0.9605
0.0009 20.9546 36000 0.3358 0.6126 0.5706 0.5909 0.9608
0.001 21.2456 36500 0.3300 0.6039 0.5851 0.5943 0.9606
0.0006 21.5367 37000 0.3355 0.6133 0.5878 0.6003 0.9612

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.1.1+cu121
  • Datasets 2.14.5
  • Tokenizers 0.19.1