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scenario-non-kd-pre-ner-full-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-pre-ner-full-mdeberta_data-univner_full55
    results: []

scenario-non-kd-pre-ner-full-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: 0.1841
  • Precision: 0.8015
  • Recall: 0.8248
  • F1: 0.8130
  • Accuracy: 0.9804

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
0.2548 0.2910 500 0.1794 0.3392 0.4613 0.3909 0.9392
0.1474 0.5821 1000 0.1201 0.5290 0.6425 0.5802 0.9597
0.0973 0.8731 1500 0.1020 0.5970 0.7570 0.6676 0.9651
0.0751 1.1641 2000 0.0905 0.6815 0.7547 0.7162 0.9719
0.062 1.4552 2500 0.0891 0.6689 0.7826 0.7213 0.9706
0.0566 1.7462 3000 0.0927 0.6774 0.8134 0.7392 0.9726
0.0506 2.0373 3500 0.0850 0.7449 0.7879 0.7658 0.9766
0.0407 2.3283 4000 0.0832 0.7512 0.7969 0.7734 0.9775
0.0374 2.6193 4500 0.0828 0.7646 0.7974 0.7806 0.9781
0.036 2.9104 5000 0.0852 0.7362 0.8211 0.7763 0.9766
0.0277 3.2014 5500 0.0903 0.7474 0.8104 0.7777 0.9773
0.0254 3.4924 6000 0.0928 0.7448 0.8149 0.7783 0.9769
0.0254 3.7835 6500 0.0881 0.7661 0.8038 0.7845 0.9789
0.0229 4.0745 7000 0.0976 0.7564 0.8204 0.7871 0.9784
0.017 4.3655 7500 0.0949 0.7647 0.8116 0.7874 0.9784
0.018 4.6566 8000 0.0992 0.7693 0.8035 0.7860 0.9779
0.0183 4.9476 8500 0.1043 0.7392 0.8373 0.7852 0.9768
0.014 5.2386 9000 0.1032 0.7642 0.8221 0.7921 0.9783
0.0127 5.5297 9500 0.1045 0.7600 0.8256 0.7914 0.9780
0.0133 5.8207 10000 0.1071 0.7724 0.8015 0.7867 0.9780
0.013 6.1118 10500 0.1089 0.7804 0.8129 0.7963 0.9792
0.0101 6.4028 11000 0.1110 0.7918 0.8071 0.7994 0.9790
0.0103 6.6938 11500 0.1102 0.7761 0.8178 0.7964 0.9783
0.0109 6.9849 12000 0.1082 0.7658 0.8273 0.7953 0.9781
0.0077 7.2759 12500 0.1196 0.7627 0.8188 0.7897 0.9784
0.008 7.5669 13000 0.1187 0.7664 0.8181 0.7914 0.9783
0.0087 7.8580 13500 0.1136 0.7819 0.8179 0.7995 0.9791
0.0082 8.1490 14000 0.1227 0.7753 0.8178 0.7960 0.9792
0.0061 8.4400 14500 0.1244 0.7782 0.8207 0.7989 0.9789
0.0067 8.7311 15000 0.1293 0.7606 0.8270 0.7924 0.9780
0.0058 9.0221 15500 0.1379 0.7671 0.8145 0.7901 0.9775
0.0056 9.3132 16000 0.1311 0.7873 0.8113 0.7991 0.9794
0.0051 9.6042 16500 0.1273 0.7852 0.8084 0.7966 0.9794
0.0048 9.8952 17000 0.1395 0.7557 0.8309 0.7915 0.9773
0.0043 10.1863 17500 0.1349 0.7967 0.8165 0.8065 0.9797
0.0042 10.4773 18000 0.1322 0.7949 0.8110 0.8029 0.9795
0.0043 10.7683 18500 0.1372 0.7900 0.8087 0.7992 0.9792
0.0046 11.0594 19000 0.1335 0.8052 0.8012 0.8032 0.9797
0.0032 11.3504 19500 0.1388 0.7785 0.8238 0.8005 0.9792
0.0036 11.6414 20000 0.1454 0.7869 0.8221 0.8041 0.9795
0.004 11.9325 20500 0.1351 0.7869 0.8097 0.7981 0.9789
0.0029 12.2235 21000 0.1549 0.7811 0.8238 0.8019 0.9791
0.0025 12.5146 21500 0.1498 0.7914 0.8160 0.8035 0.9794
0.0031 12.8056 22000 0.1470 0.7946 0.8142 0.8042 0.9793
0.0034 13.0966 22500 0.1481 0.7878 0.8137 0.8006 0.9793
0.0022 13.3877 23000 0.1500 0.7884 0.8225 0.8051 0.9794
0.0026 13.6787 23500 0.1525 0.7870 0.8277 0.8068 0.9796
0.003 13.9697 24000 0.1482 0.7918 0.8205 0.8059 0.9798
0.0021 14.2608 24500 0.1533 0.7786 0.8234 0.8004 0.9792
0.0023 14.5518 25000 0.1453 0.7835 0.8238 0.8032 0.9797
0.0024 14.8428 25500 0.1506 0.7873 0.8277 0.8070 0.9796
0.0021 15.1339 26000 0.1561 0.7996 0.8169 0.8082 0.9798
0.0018 15.4249 26500 0.1603 0.7859 0.8243 0.8046 0.9793
0.0019 15.7159 27000 0.1665 0.7835 0.8189 0.8008 0.9794
0.0019 16.0070 27500 0.1543 0.7921 0.8153 0.8036 0.9796
0.0017 16.2980 28000 0.1694 0.7956 0.8130 0.8042 0.9796
0.0018 16.5891 28500 0.1573 0.7930 0.8100 0.8014 0.9794
0.0019 16.8801 29000 0.1544 0.7930 0.8220 0.8072 0.9795
0.0016 17.1711 29500 0.1628 0.7900 0.8237 0.8065 0.9795
0.0014 17.4622 30000 0.1609 0.7875 0.8208 0.8038 0.9793
0.0014 17.7532 30500 0.1637 0.7844 0.8241 0.8038 0.9793
0.0016 18.0442 31000 0.1647 0.7964 0.8124 0.8043 0.9798
0.0014 18.3353 31500 0.1690 0.7962 0.8149 0.8054 0.9800
0.0012 18.6263 32000 0.1759 0.7837 0.8254 0.8040 0.9792
0.001 18.9173 32500 0.1764 0.7950 0.8159 0.8053 0.9797
0.0011 19.2084 33000 0.1684 0.8022 0.8106 0.8064 0.9802
0.001 19.4994 33500 0.1723 0.7944 0.8143 0.8042 0.9797
0.0014 19.7905 34000 0.1637 0.7886 0.8300 0.8088 0.9801
0.0013 20.0815 34500 0.1633 0.7885 0.8201 0.8040 0.9799
0.001 20.3725 35000 0.1682 0.7955 0.8251 0.8101 0.9799
0.0009 20.6636 35500 0.1735 0.7956 0.8222 0.8087 0.9798
0.0009 20.9546 36000 0.1765 0.7898 0.8231 0.8061 0.9796
0.0012 21.2456 36500 0.1754 0.7977 0.8243 0.8108 0.9800
0.0006 21.5367 37000 0.1710 0.7978 0.8176 0.8076 0.9799
0.0008 21.8277 37500 0.1650 0.8035 0.8256 0.8144 0.9800
0.0007 22.1187 38000 0.1702 0.7893 0.8341 0.8111 0.9795
0.0007 22.4098 38500 0.1724 0.8086 0.8067 0.8077 0.9801
0.0008 22.7008 39000 0.1714 0.7951 0.8224 0.8085 0.9800
0.0007 22.9919 39500 0.1757 0.8010 0.8194 0.8101 0.9803
0.0005 23.2829 40000 0.1776 0.8002 0.8261 0.8129 0.9802
0.0007 23.5739 40500 0.1771 0.7978 0.8273 0.8123 0.9802
0.0007 23.8650 41000 0.1800 0.7984 0.8233 0.8106 0.9801
0.0007 24.1560 41500 0.1761 0.7970 0.8261 0.8113 0.9799
0.0005 24.4470 42000 0.1729 0.8041 0.8220 0.8129 0.9801
0.0007 24.7381 42500 0.1753 0.8066 0.8214 0.8139 0.9802
0.0004 25.0291 43000 0.1812 0.7875 0.8308 0.8085 0.9796
0.0006 25.3201 43500 0.1773 0.7991 0.8240 0.8113 0.9801
0.0005 25.6112 44000 0.1771 0.7996 0.8168 0.8081 0.9801
0.0005 25.9022 44500 0.1780 0.7986 0.8266 0.8123 0.9801
0.0004 26.1932 45000 0.1788 0.7999 0.8227 0.8112 0.9802
0.0005 26.4843 45500 0.1792 0.7981 0.8277 0.8127 0.9802
0.0004 26.7753 46000 0.1807 0.7959 0.8250 0.8102 0.9801
0.0004 27.0664 46500 0.1807 0.8079 0.8217 0.8147 0.9804
0.0005 27.3574 47000 0.1818 0.8013 0.8254 0.8132 0.9803
0.0005 27.6484 47500 0.1814 0.7985 0.8220 0.8100 0.9802
0.0003 27.9395 48000 0.1831 0.8010 0.8261 0.8134 0.9803
0.0003 28.2305 48500 0.1836 0.8051 0.8222 0.8136 0.9803
0.0002 28.5215 49000 0.1857 0.8028 0.8237 0.8131 0.9803
0.0007 28.8126 49500 0.1839 0.7976 0.8272 0.8121 0.9803
0.0003 29.1036 50000 0.1839 0.8037 0.8243 0.8139 0.9803
0.0002 29.3946 50500 0.1842 0.8026 0.8238 0.8131 0.9804
0.0002 29.6857 51000 0.1843 0.8004 0.8247 0.8124 0.9803
0.0003 29.9767 51500 0.1841 0.8015 0.8248 0.8130 0.9804

Framework versions

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