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bert-base-cased_legal_ner_finetuned

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  1. README.md +87 -0
  2. config.json +48 -0
  3. model.safetensors +3 -0
  4. runs/Sep06_16-03-36_Khalids-MBP/events.out.tfevents.1725653017.Khalids-MBP.23384.12 +3 -0
  5. runs/Sep06_16-03-36_Khalids-MBP/events.out.tfevents.1725653849.Khalids-MBP.23384.13 +3 -0
  6. special_tokens_map.json +7 -0
  7. tokenizer.json +0 -0
  8. tokenizer_config.json +56 -0
  9. training_args.bin +3 -0
  10. vocab.txt +0 -0
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: google-bert/bert-base-cased
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: bert-base-cased_legal_ner_finetuned
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # bert-base-cased_legal_ner_finetuned
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+
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+ This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.3018
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+ - Law Precision: 0.7364
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+ - Law Recall: 0.8261
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+ - Law F1: 0.7787
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+ - Law Number: 115
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+ - Violated by Precision: 0.8525
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+ - Violated by Recall: 0.6933
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+ - Violated by F1: 0.7647
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+ - Violated by Number: 75
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+ - Violated on Precision: 0.4688
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+ - Violated on Recall: 0.4286
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+ - Violated on F1: 0.4478
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+ - Violated on Number: 70
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+ - Violation Precision: 0.6323
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+ - Violation Recall: 0.7251
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+ - Violation F1: 0.6755
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+ - Violation Number: 491
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+ - Overall Precision: 0.6524
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+ - Overall Recall: 0.7097
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+ - Overall F1: 0.6798
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+ - Overall Accuracy: 0.9439
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 2e-05
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 500
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+ - num_epochs: 10
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+
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+ ### Training results
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+
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+ | 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 |
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+ |:-------------:|:-----:|:----:|:---------------:|:-------------:|:----------:|:------:|:----------:|:---------------------:|:------------------:|:--------------:|:------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:-------------------:|:----------------:|:------------:|:----------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.44.0
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+ - Pytorch 2.4.0
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+ - Datasets 2.21.0
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+ - Tokenizers 0.19.1
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