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--- |
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license: apache-2.0 |
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base_model: google-bert/bert-base-uncased |
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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-uncased_legal_ner_finetuned |
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results: [] |
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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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# bert-base-uncased_legal_ner_finetuned |
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This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2878 |
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- Law Precision: 0.7983 |
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- Law Recall: 0.8716 |
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- Law F1: 0.8333 |
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- Law Number: 109 |
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- Violated by Precision: 0.7681 |
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- Violated by Recall: 0.7465 |
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- Violated by F1: 0.7571 |
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- Violated by Number: 71 |
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- Violated on Precision: 0.4143 |
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- Violated on Recall: 0.4143 |
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- Violated on F1: 0.4143 |
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- Violated on Number: 70 |
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- Violation Precision: 0.59 |
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- Violation Recall: 0.6941 |
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- Violation F1: 0.6378 |
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- Violation Number: 425 |
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- Overall Precision: 0.6227 |
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- Overall Recall: 0.6993 |
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- Overall F1: 0.6588 |
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- Overall Accuracy: 0.9462 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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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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### Training results |
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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 | 0.8260 | 0.0 | 0.0 | 0.0 | 109 | 0.0 | 0.0 | 0.0 | 71 | 0.0 | 0.0 | 0.0 | 70 | 0.0 | 0.0 | 0.0 | 425 | 0.0 | 0.0 | 0.0 | 0.7656 | |
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| No log | 2.0 | 170 | 0.4451 | 0.0 | 0.0 | 0.0 | 109 | 0.0 | 0.0 | 0.0 | 71 | 0.0 | 0.0 | 0.0 | 70 | 0.1204 | 0.1624 | 0.1383 | 425 | 0.1204 | 0.1022 | 0.1106 | 0.8766 | |
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| No log | 3.0 | 255 | 0.3153 | 0.1724 | 0.0917 | 0.1198 | 109 | 0.0 | 0.0 | 0.0 | 71 | 0.0 | 0.0 | 0.0 | 70 | 0.3142 | 0.36 | 0.3355 | 425 | 0.2991 | 0.2415 | 0.2672 | 0.9067 | |
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| No log | 4.0 | 340 | 0.2416 | 0.6574 | 0.6514 | 0.6544 | 109 | 0.0 | 0.0 | 0.0 | 71 | 0.16 | 0.0571 | 0.0842 | 70 | 0.4496 | 0.5671 | 0.5016 | 425 | 0.4470 | 0.4681 | 0.4573 | 0.9286 | |
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| No log | 5.0 | 425 | 0.2185 | 0.7768 | 0.7982 | 0.7873 | 109 | 0.6491 | 0.5211 | 0.5781 | 71 | 0.3125 | 0.2857 | 0.2985 | 70 | 0.5019 | 0.6329 | 0.5598 | 425 | 0.5371 | 0.6119 | 0.5720 | 0.9412 | |
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| 0.5331 | 6.0 | 510 | 0.2399 | 0.6767 | 0.8257 | 0.7438 | 109 | 0.6842 | 0.7324 | 0.7075 | 71 | 0.2841 | 0.3571 | 0.3165 | 70 | 0.5820 | 0.7012 | 0.6361 | 425 | 0.5748 | 0.6889 | 0.6267 | 0.9416 | |
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| 0.5331 | 7.0 | 595 | 0.2407 | 0.7603 | 0.8440 | 0.8 | 109 | 0.7286 | 0.7183 | 0.7234 | 71 | 0.4348 | 0.4286 | 0.4317 | 70 | 0.5752 | 0.6753 | 0.6212 | 425 | 0.6061 | 0.6815 | 0.6416 | 0.9441 | |
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| 0.5331 | 8.0 | 680 | 0.2610 | 0.7661 | 0.8716 | 0.8155 | 109 | 0.6 | 0.7606 | 0.6708 | 71 | 0.3043 | 0.4 | 0.3457 | 70 | 0.5948 | 0.7012 | 0.6436 | 425 | 0.5886 | 0.7037 | 0.6410 | 0.9428 | |
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| 0.5331 | 9.0 | 765 | 0.2790 | 0.744 | 0.8532 | 0.7949 | 109 | 0.8667 | 0.7324 | 0.7939 | 71 | 0.3788 | 0.3571 | 0.3676 | 70 | 0.5812 | 0.6824 | 0.6277 | 425 | 0.6133 | 0.6815 | 0.6456 | 0.9461 | |
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| 0.5331 | 10.0 | 850 | 0.2878 | 0.7983 | 0.8716 | 0.8333 | 109 | 0.7681 | 0.7465 | 0.7571 | 71 | 0.4143 | 0.4143 | 0.4143 | 70 | 0.59 | 0.6941 | 0.6378 | 425 | 0.6227 | 0.6993 | 0.6588 | 0.9462 | |
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### Framework versions |
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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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