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legal_bert_small_summarized
wiorz/legal_bert_small_summarized
legal_bert_small_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 21, 2023
- Updated
- May 23, 2023
- SHA
- 58b0d377
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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_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: 2.0708
- Accuracy: 0.815
- Precision: 0.5
- Recall: 0.1622
- F1: 0.2449
- D-index: 1.5040
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 | 0.4799 | 0.815 | 0.0 | 0.0 | 0.0 | 1.4449 |
| No log | 2.0 | 400 | 0.5646 | 0.815 | 0.0 | 0.0 | 0.0 | 1.4449 |
| 0.5383 | 3.0 | 600 | 0.5505 | 0.815 | 0.0 | 0.0 | 0.0 | 1.4449 |
| 0.5383 | 4.0 | 800 | 0.4502 | 0.815 | 0.5 | 0.2162 | 0.3019 | 1.5231 |
| 0.5116 | 5.0 | 1000 | 0.6932 | 0.805 | 0.4444 | 0.2162 | 0.2909 | 1.5096 |
| 0.5116 | 6.0 | 1200 | 1.0173 | 0.795 | 0.4231 | 0.2973 | 0.3492 | 1.5244 |
| 0.5116 | 7.0 | 1400 | 1.2308 | 0.82 | 0.5714 | 0.1081 | 0.1818 | 1.4914 |
| 0.1778 | 8.0 | 1600 | 1.4035 | 0.815 | 0.5 | 0.2432 | 0.3273 | 1.5326 |
| 0.1778 | 9.0 | 1800 | 1.6336 | 0.815 | 0.5 | 0.1622 | 0.2449 | 1.5040 |
| 0.0255 | 10.0 | 2000 | 1.7291 | 0.82 | 0.5385 | 0.1892 | 0.28 | 1.5204 |
| 0.0255 | 11.0 | 2200 | 1.7801 | 0.825 | 0.5714 | 0.2162 | 0.3137 | 1.5367 |
| 0.0255 | 12.0 | 2400 | 1.8364 | 0.825 | 0.5714 | 0.2162 | 0.3137 | 1.5367 |
| 0.0 | 13.0 | 2600 | 1.8688 | 0.825 | 0.5714 | 0.2162 | 0.3137 | 1.5367 |
| 0.0 | 14.0 | 2800 | 1.9549 | 0.815 | 0.5 | 0.1622 | 0.2449 | 1.5040 |
| 0.0 | 15.0 | 3000 | 2.0022 | 0.815 | 0.5 | 0.1622 | 0.2449 | 1.5040 |
| 0.0 | 16.0 | 3200 | 1.9795 | 0.82 | 0.5385 | 0.1892 | 0.28 | 1.5204 |
| 0.0 | 17.0 | 3400 | 2.0438 | 0.815 | 0.5 | 0.1622 | 0.2449 | 1.5040 |
| 0.0 | 18.0 | 3600 | 2.0603 | 0.815 | 0.5 | 0.1622 | 0.2449 | 1.5040 |
| 0.0 | 19.0 | 3800 | 2.0722 | 0.815 | 0.5 | 0.1622 | 0.2449 | 1.5040 |
| 0.0014 | 20.0 | 4000 | 2.0708 | 0.815 | 0.5 | 0.1622 | 0.2449 | 1.5040 |
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 21, 2023
- Updated
- May 23, 2023