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bert-base-cased_legal_ner_finetuned

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metadata
license: apache-2.0
base_model: google-bert/bert-base-cased
tags:
  - generated_from_trainer
model-index:
  - name: bert-base-cased_legal_ner_finetuned
    results: []

bert-base-cased_legal_ner_finetuned

This model is a fine-tuned version of google-bert/bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3018
  • Law Precision: 0.7364
  • Law Recall: 0.8261
  • Law F1: 0.7787
  • Law Number: 115
  • Violated by Precision: 0.8525
  • Violated by Recall: 0.6933
  • Violated by F1: 0.7647
  • Violated by Number: 75
  • Violated on Precision: 0.4688
  • Violated on Recall: 0.4286
  • Violated on F1: 0.4478
  • Violated on Number: 70
  • Violation Precision: 0.6323
  • Violation Recall: 0.7251
  • Violation F1: 0.6755
  • Violation Number: 491
  • Overall Precision: 0.6524
  • Overall Recall: 0.7097
  • Overall F1: 0.6798
  • Overall Accuracy: 0.9439

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
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss Law Precision Law Recall Law F1 Law Number Violated by Precision Violated by Recall Violated by F1 Violated by Number Violated on Precision Violated on Recall Violated on F1 Violated on Number Violation Precision Violation Recall Violation F1 Violation Number Overall Precision Overall Recall Overall F1 Overall Accuracy
No log 1.0 85 0.8046 0.0 0.0 0.0 115 0.0 0.0 0.0 75 0.0 0.0 0.0 70 0.0 0.0 0.0 491 0.0 0.0 0.0 0.7619
No log 2.0 170 0.4050 0.0 0.0 0.0 115 0.0 0.0 0.0 75 0.0 0.0 0.0 70 0.1835 0.2037 0.1931 491 0.1835 0.1332 0.1543 0.8819
No log 3.0 255 0.2861 0.6111 0.4783 0.5366 115 0.1818 0.0533 0.0825 75 0.4 0.0571 0.1000 70 0.4345 0.5540 0.4870 491 0.4479 0.4461 0.4470 0.9130
No log 4.0 340 0.2552 0.75 0.7043 0.7265 115 0.5625 0.36 0.4390 75 0.3429 0.1714 0.2286 70 0.4924 0.5927 0.5379 491 0.5256 0.5473 0.5362 0.9257
No log 5.0 425 0.2676 0.7154 0.7652 0.7395 115 0.7308 0.5067 0.5984 75 0.2778 0.1429 0.1887 70 0.5368 0.6090 0.5706 491 0.5664 0.5792 0.5727 0.9300
0.4786 6.0 510 0.2663 0.6767 0.7826 0.7258 115 0.7903 0.6533 0.7153 75 0.3684 0.4 0.3836 70 0.6155 0.7271 0.6667 491 0.6157 0.6977 0.6542 0.9366
0.4786 7.0 595 0.2352 0.6957 0.8348 0.7589 115 0.7941 0.72 0.7552 75 0.4242 0.4 0.4118 70 0.5799 0.7169 0.6412 491 0.6030 0.7057 0.6503 0.9412
0.4786 8.0 680 0.2728 0.6835 0.8261 0.7480 115 0.7857 0.7333 0.7586 75 0.3596 0.4571 0.4025 70 0.5916 0.7434 0.6588 491 0.5978 0.7284 0.6567 0.9415
0.4786 9.0 765 0.2952 0.7385 0.8348 0.7837 115 0.8088 0.7333 0.7692 75 0.5 0.5 0.5 70 0.6246 0.7352 0.6754 491 0.6466 0.7284 0.6850 0.9433
0.4786 10.0 850 0.3018 0.7364 0.8261 0.7787 115 0.8525 0.6933 0.7647 75 0.4688 0.4286 0.4478 70 0.6323 0.7251 0.6755 491 0.6524 0.7097 0.6798 0.9439

Framework versions

  • Transformers 4.44.0
  • Pytorch 2.4.0
  • Datasets 2.21.0
  • Tokenizers 0.19.1