generated_from_trainer roberta Greek ner

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roberta-el-ner4

This model is a fine-tuned version of cvcio/roberta-el-news on the elNER dataset. It achieves the following results on the evaluation set:

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

More information needed

Training hyperparameters

The following hyperparameters were used during training:

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
0.1723 1.87 250 0.0716 0.7937 0.8657 0.8281 0.9786
0.0372 3.73 500 0.0497 0.8587 0.9215 0.8890 0.9852
0.0243 5.6 750 0.0524 0.8746 0.9263 0.8997 0.9867
0.0114 7.46 1000 0.0564 0.9116 0.9218 0.9167 0.9883
0.0071 9.33 1250 0.0602 0.9019 0.9309 0.9161 0.9881
0.0037 11.19 1500 0.0667 0.9074 0.9306 0.9188 0.9885
0.003 13.06 1750 0.0688 0.9001 0.9306 0.9151 0.9883
0.0019 14.93 2000 0.0724 0.9082 0.9229 0.9155 0.9883
0.0022 16.79 2250 0.0745 0.9159 0.9169 0.9164 0.9878
0.0016 18.66 2500 0.0727 0.9068 0.9249 0.9158 0.9880
0.0018 20.52 2750 0.0732 0.9088 0.9272 0.9179 0.9887
0.0014 22.39 3000 0.0767 0.9017 0.9243 0.9129 0.9876
0.0012 24.25 3250 0.0745 0.9072 0.9206 0.9139 0.9882
0.0011 26.12 3500 0.0790 0.8995 0.9297 0.9144 0.9878
0.0008 27.99 3750 0.0786 0.9081 0.9275 0.9177 0.9883
0.0011 29.85 4000 0.0775 0.9091 0.9277 0.9183 0.9885
0.0011 31.72 4250 0.0851 0.9005 0.9269 0.9135 0.9879
0.0007 33.58 4500 0.0848 0.9041 0.9223 0.9131 0.9876
0.0006 35.45 4750 0.0842 0.9082 0.9263 0.9172 0.9881
0.0005 37.31 5000 0.0851 0.9085 0.9266 0.9175 0.9881
0.0004 39.18 5250 0.0878 0.9035 0.9272 0.9152 0.9879
0.0004 41.04 5500 0.0856 0.9091 0.9275 0.9182 0.9885
0.0004 42.91 5750 0.0870 0.9099 0.9255 0.9176 0.9884
0.0005 44.78 6000 0.0860 0.9010 0.9269 0.9138 0.9882
0.0004 46.64 6250 0.0851 0.9114 0.9246 0.9179 0.9884
0.0003 48.51 6500 0.0899 0.9058 0.9252 0.9154 0.9884
0.0002 50.37 6750 0.0898 0.9050 0.9294 0.9171 0.9882
0.0002 52.24 7000 0.0890 0.9104 0.9252 0.9177 0.9884
0.0002 54.1 7250 0.0898 0.9052 0.9260 0.9155 0.9879
0.0002 55.97 7500 0.0894 0.9080 0.9263 0.9171 0.9883
0.0001 57.84 7750 0.0910 0.9046 0.9277 0.9160 0.9883
0.0003 59.7 8000 0.0903 0.9041 0.9283 0.9161 0.9882

Eval results

Precision Recall F1 Accuracy
eval 0.9116 0.9218 0.9167 0.9883
test 0.9022 0.9107 0.9064 0.9861

Framework versions

Authors

Dimitris Papaevagelou - @andefined

About Us

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