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legal_bert_small_defined_summarized
wiorz/legal_bert_small_defined_summarized
legal_bert_small_defined_summarized is a text classification model from wiorz. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as cc-by-sa-4.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- cc-by-sa-4.0
- Model type
- bert
- Access
- Public
- Created
- May 23, 2023
- Updated
- May 23, 2023
- SHA
- 1d45c6de
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Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
legal_bert_small_defined_summarized
This model is a fine-tuned version of nlpaueb/legal-bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.4178
- Accuracy: 0.87
- Precision: 0.6
- Recall: 0.2143
- F1: 0.3158
- D-index: 1.5771
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: 5e-05
- train_batch_size: 4
- 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: 1600
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | D-index |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 200 | 0.4008 | 0.86 | 0.0 | 0.0 | 0.0 | 1.4803 |
| No log | 2.0 | 400 | 0.3871 | 0.86 | 0.0 | 0.0 | 0.0 | 1.4803 |
| 0.5179 | 3.0 | 600 | 0.4293 | 0.87 | 0.625 | 0.1786 | 0.2778 | 1.5635 |
| 0.5179 | 4.0 | 800 | 0.6702 | 0.87 | 0.625 | 0.1786 | 0.2778 | 1.5635 |
| 0.3816 | 5.0 | 1000 | 0.7388 | 0.865 | 0.5455 | 0.2143 | 0.3077 | 1.5706 |
| 0.3816 | 6.0 | 1200 | 1.0422 | 0.86 | 0.5 | 0.1786 | 0.2632 | 1.5504 |
| 0.3816 | 7.0 | 1400 | 1.0804 | 0.875 | 0.7143 | 0.1786 | 0.2857 | 1.5700 |
| 0.0567 | 8.0 | 1600 | 1.1490 | 0.875 | 0.6364 | 0.25 | 0.3590 | 1.5970 |
| 0.0567 | 9.0 | 1800 | 1.3190 | 0.865 | 0.5556 | 0.1786 | 0.2703 | 1.5570 |
| 0.0125 | 10.0 | 2000 | 1.4220 | 0.835 | 0.3913 | 0.3214 | 0.3529 | 1.5718 |
| 0.0125 | 11.0 | 2200 | 1.3567 | 0.855 | 0.4706 | 0.2857 | 0.3556 | 1.5845 |
| 0.0125 | 12.0 | 2400 | 1.3349 | 0.875 | 0.7143 | 0.1786 | 0.2857 | 1.5700 |
| 0.0021 | 13.0 | 2600 | 1.3494 | 0.87 | 0.5714 | 0.2857 | 0.3810 | 1.6038 |
| 0.0021 | 14.0 | 2800 | 1.3747 | 0.87 | 0.6 | 0.2143 | 0.3158 | 1.5771 |
| 0.0 | 15.0 | 3000 | 1.3890 | 0.87 | 0.6 | 0.2143 | 0.3158 | 1.5771 |
| 0.0 | 16.0 | 3200 | 1.4069 | 0.875 | 0.6667 | 0.2143 | 0.3243 | 1.5835 |
| 0.0 | 17.0 | 3400 | 1.4185 | 0.875 | 0.6667 | 0.2143 | 0.3243 | 1.5835 |
| 0.0 | 18.0 | 3600 | 1.3945 | 0.865 | 0.5385 | 0.25 | 0.3415 | 1.5840 |
| 0.0 | 19.0 | 3800 | 1.3921 | 0.87 | 0.6 | 0.2143 | 0.3158 | 1.5771 |
| 0.0037 | 20.0 | 4000 | 1.4178 | 0.87 | 0.6 | 0.2143 | 0.3158 | 1.5771 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- cc-by-sa-4.0
- Created
- May 23, 2023
- Updated
- May 23, 2023