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ner_bert_model

This model is a fine-tuned version of distilbert-base-cased on the shipping_label_ner dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4675
  • Precision: 0.8193
  • Recall: 0.9067
  • F1: 0.8608
  • Accuracy: 0.9040

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: 2
  • seed: 42
  • 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
No log 1.0 7 1.9567 0.0 0.0 0.0 0.4294
No log 2.0 14 1.7382 1.0 0.0133 0.0263 0.4350
No log 3.0 21 1.5156 0.56 0.1867 0.28 0.5424
No log 4.0 28 1.3070 0.5185 0.3733 0.4341 0.6215
No log 5.0 35 1.1073 0.6792 0.48 0.5625 0.6667
No log 6.0 42 0.9590 0.6970 0.6133 0.6525 0.7288
No log 7.0 49 0.8036 0.7324 0.6933 0.7123 0.7853
No log 8.0 56 0.7173 0.6860 0.7867 0.7329 0.8305
No log 9.0 63 0.5963 0.7778 0.84 0.8077 0.8814
No log 10.0 70 0.5354 0.7901 0.8533 0.8205 0.8870
No log 11.0 77 0.5048 0.8 0.8533 0.8258 0.8814
No log 12.0 84 0.4992 0.8293 0.9067 0.8662 0.9096
No log 13.0 91 0.4745 0.8205 0.8533 0.8366 0.8927
No log 14.0 98 0.4489 0.8608 0.9067 0.8831 0.9153
No log 15.0 105 0.4236 0.8608 0.9067 0.8831 0.9153
No log 16.0 112 0.4621 0.8193 0.9067 0.8608 0.9096
No log 17.0 119 0.4417 0.85 0.9067 0.8774 0.9209
No log 18.0 126 0.4642 0.8095 0.9067 0.8553 0.9040
No log 19.0 133 0.4244 0.85 0.9067 0.8774 0.9096
No log 20.0 140 0.4731 0.8193 0.9067 0.8608 0.9096
No log 21.0 147 0.4697 0.8193 0.9067 0.8608 0.9040
No log 22.0 154 0.4330 0.8293 0.9067 0.8662 0.9096
No log 23.0 161 0.4531 0.8193 0.9067 0.8608 0.9040
No log 24.0 168 0.4433 0.8193 0.9067 0.8608 0.9040
No log 25.0 175 0.4477 0.8095 0.9067 0.8553 0.9040
No log 26.0 182 0.4446 0.8293 0.9067 0.8662 0.9096
No log 27.0 189 0.4578 0.8293 0.9067 0.8662 0.9096
No log 28.0 196 0.4640 0.8293 0.9067 0.8662 0.9096
No log 29.0 203 0.4683 0.8193 0.9067 0.8608 0.9040
No log 30.0 210 0.4675 0.8193 0.9067 0.8608 0.9040

Framework versions

  • Transformers 4.39.1
  • Pytorch 2.2.1+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2
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Evaluation results