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scideberta-cs-tdm-pretrained-finetuned-ner

Token Classification
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PyTorch
deberta
Generated from Trainer
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scideberta-cs-tdm-pretrained-finetuned-ner / README.md
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metadata
tags:
  - generated_from_trainer
datasets:
  - generator
base_model: sohamtiwari3120/scideberta-cs-tdm-pretrained
model-index:
  - name: scideberta-cs-tdm-pretrained-finetuned-ner
    results: []

scideberta-cs-tdm-pretrained-finetuned-ner

This model is a fine-tuned version of sohamtiwari3120/scideberta-cs-tdm-pretrained on the generator dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6836
  • Overall Precision: 0.5912
  • Overall Recall: 0.6850
  • Overall F1: 0.6347
  • Overall Accuracy: 0.9609
  • Datasetname F1: 0.5882
  • Hyperparametername F1: 0.6897
  • Hyperparametervalue F1: 0.7619
  • Methodname F1: 0.6525
  • Metricname F1: 0.7500
  • Metricvalue F1: 0.6452
  • Taskname F1: 0.5370

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: 2e-05
  • 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 Overall Precision Overall Recall Overall F1 Overall Accuracy Datasetname F1 Hyperparametername F1 Hyperparametervalue F1 Methodname F1 Metricname F1 Metricvalue F1 Taskname F1
No log 1.0 132 0.3507 0.3972 0.6870 0.5034 0.9410 0.4370 0.5441 0.5814 0.6124 0.5604 0.6207 0.3724
No log 2.0 264 0.3079 0.4066 0.7520 0.5278 0.9430 0.4138 0.5380 0.6222 0.5895 0.625 0.7273 0.4340
No log 3.0 396 0.3740 0.5007 0.7195 0.5905 0.9535 0.4882 0.6777 0.7500 0.6254 0.6747 0.7097 0.4962
0.4014 4.0 528 0.4072 0.5161 0.7154 0.5997 0.9540 0.5167 0.6612 0.6374 0.6337 0.6753 0.6061 0.5341
0.4014 5.0 660 0.4088 0.5590 0.7317 0.6338 0.9582 0.5660 0.6667 0.7397 0.6250 0.7226 0.75 0.5794
0.4014 6.0 792 0.4810 0.5201 0.7093 0.6002 0.9550 0.4874 0.5970 0.6506 0.6207 0.6708 0.6250 0.5756
0.4014 7.0 924 0.5288 0.5403 0.6809 0.6025 0.9576 0.4915 0.6500 0.6133 0.6255 0.7006 0.7879 0.5389
0.0912 8.0 1056 0.5281 0.5468 0.6890 0.6097 0.9574 0.5370 0.7143 0.6866 0.5854 0.6939 0.7742 0.5491
0.0912 9.0 1188 0.4744 0.5371 0.7358 0.6209 0.9560 0.5370 0.6341 0.6753 0.6554 0.6795 0.7059 0.5699
0.0912 10.0 1320 0.5498 0.5686 0.7073 0.6304 0.9586 0.5370 0.6349 0.7500 0.6553 0.7152 0.7742 0.5573
0.0912 11.0 1452 0.6424 0.5857 0.7012 0.6383 0.9597 0.56 0.6789 0.7246 0.6667 0.6974 0.6875 0.5757
0.0354 12.0 1584 0.5867 0.5641 0.6890 0.6203 0.9585 0.5185 0.6496 0.7213 0.6619 0.7152 0.7333 0.5402
0.0354 13.0 1716 0.5500 0.5667 0.6992 0.6260 0.9592 0.5524 0.6829 0.7222 0.6621 0.6466 0.7333 0.5607
0.0354 14.0 1848 0.5743 0.5780 0.7154 0.6394 0.9596 0.5283 0.6833 0.7222 0.6644 0.6716 0.7742 0.5960
0.0354 15.0 1980 0.6836 0.5912 0.6850 0.6347 0.9609 0.5882 0.6897 0.7619 0.6525 0.7500 0.6452 0.5370

Framework versions

  • Transformers 4.23.1
  • Pytorch 1.12.1+cu102
  • Datasets 2.6.1
  • Tokenizers 0.13.1