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---
license: cc-by-sa-4.0
base_model: nlpaueb/legal-bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Flavio
results: []
---
<!-- 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. -->
# Flavio
This model is a fine-tuned version of [nlpaueb/legal-bert-base-uncased](https://huggingface.co/nlpaueb/legal-bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3914
- Accuracy: 0.9150
- F1 Macro: 0.8231
- F1 Class 0: 0.9472
- F1 Class 1: 0.6667
- F1 Class 2: 0.9259
- F1 Class 3: 0.8421
- F1 Class 4: 0.9
- F1 Class 5: 0.9615
- F1 Class 6: 0.8
- F1 Class 7: 0.9556
- F1 Class 8: 0.9655
- F1 Class 9: 0.8621
- F1 Class 10: 0.8924
- F1 Class 11: 0.7143
- F1 Class 12: 0.8101
- F1 Class 13: 0.75
- F1 Class 14: 0.8889
- F1 Class 15: 0.7500
- F1 Class 16: 0.0
- F1 Class 17: 0.9880
- F1 Class 18: 0.9180
- F1 Class 19: 0.9231
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Class 0 | F1 Class 1 | F1 Class 2 | F1 Class 3 | F1 Class 4 | F1 Class 5 | F1 Class 6 | F1 Class 7 | F1 Class 8 | F1 Class 9 | F1 Class 10 | F1 Class 11 | F1 Class 12 | F1 Class 13 | F1 Class 14 | F1 Class 15 | F1 Class 16 | F1 Class 17 | F1 Class 18 | F1 Class 19 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|
| 1.2343 | 0.39 | 250 | 0.7445 | 0.8363 | 0.5500 | 0.875 | 0.0 | 0.8959 | 0.8421 | 0.0769 | 0.6818 | 0.6667 | 0.9556 | 0.9492 | 0.6190 | 0.8339 | 0.0 | 0.7442 | 0.2000 | 0.8267 | 0.0 | 0.0 | 0.9760 | 0.8571 | 0.0 |
| 0.5654 | 0.79 | 500 | 0.5466 | 0.8690 | 0.6846 | 0.9124 | 0.0 | 0.9189 | 0.8421 | 0.7660 | 0.8302 | 0.6531 | 0.9663 | 0.9310 | 0.7353 | 0.8580 | 0.0 | 0.7564 | 0.8889 | 0.8272 | 0.1 | 0.0 | 0.9759 | 0.8070 | 0.9231 |
| 0.439 | 1.18 | 750 | 0.4626 | 0.8832 | 0.7211 | 0.9209 | 0.0 | 0.9217 | 0.8421 | 0.8 | 0.9057 | 0.6667 | 0.9556 | 0.9455 | 0.8000 | 0.8554 | 0.2857 | 0.7799 | 0.8889 | 0.8462 | 0.1905 | 0.0 | 0.9759 | 0.9180 | 0.9231 |
| 0.3397 | 1.57 | 1000 | 0.4744 | 0.8885 | 0.7457 | 0.9207 | 0.0 | 0.9327 | 0.8421 | 0.7826 | 0.8364 | 0.7547 | 0.9663 | 0.9655 | 0.7273 | 0.8735 | 0.6667 | 0.8077 | 0.8889 | 0.8553 | 0.32 | 0.0 | 0.9730 | 0.8772 | 0.9231 |
| 0.3351 | 1.97 | 1250 | 0.4128 | 0.8938 | 0.7784 | 0.9350 | 0.4 | 0.9217 | 0.8000 | 0.8108 | 0.8519 | 0.6939 | 0.9663 | 0.9474 | 0.7719 | 0.8563 | 0.7692 | 0.8199 | 0.8889 | 0.8903 | 0.4800 | 0.0 | 0.9790 | 0.8621 | 0.9231 |
| 0.2384 | 2.36 | 1500 | 0.3982 | 0.9071 | 0.8016 | 0.9431 | 0.4 | 0.9259 | 0.8421 | 0.9048 | 0.8772 | 0.8333 | 0.9556 | 0.9655 | 0.8302 | 0.8810 | 0.6667 | 0.7922 | 0.8889 | 0.8961 | 0.5882 | 0.0 | 0.9850 | 0.9333 | 0.9231 |
| 0.2309 | 2.75 | 1750 | 0.3741 | 0.9133 | 0.8191 | 0.9494 | 0.6667 | 0.9266 | 0.8421 | 0.8780 | 0.9091 | 0.8197 | 0.9556 | 0.9655 | 0.84 | 0.8831 | 0.625 | 0.8026 | 0.8235 | 0.9032 | 0.7647 | 0.0 | 0.9880 | 0.9153 | 0.9231 |
| 0.2243 | 3.14 | 2000 | 0.3962 | 0.9080 | 0.8146 | 0.9435 | 0.5714 | 0.9302 | 0.8421 | 0.9 | 0.9804 | 0.7059 | 0.9556 | 0.9492 | 0.8727 | 0.8765 | 0.7692 | 0.8050 | 0.8235 | 0.8889 | 0.6452 | 0.0 | 0.9760 | 0.9333 | 0.9231 |
| 0.1781 | 3.54 | 2250 | 0.3775 | 0.9133 | 0.8137 | 0.9418 | 0.4 | 0.9395 | 0.8421 | 0.9 | 0.9091 | 0.8814 | 0.9556 | 0.9655 | 0.8421 | 0.8952 | 0.7143 | 0.8077 | 0.8235 | 0.8679 | 0.7500 | 0.0 | 0.9816 | 0.9333 | 0.9231 |
| 0.169 | 3.93 | 2500 | 0.4092 | 0.9080 | 0.8157 | 0.9395 | 0.6667 | 0.9224 | 0.8421 | 0.9 | 0.9091 | 0.8136 | 0.9556 | 0.9655 | 0.8621 | 0.8825 | 0.6667 | 0.8077 | 0.75 | 0.8701 | 0.7500 | 0.0 | 0.9879 | 0.9 | 0.9231 |
| 0.1406 | 4.32 | 2750 | 0.3886 | 0.9097 | 0.8244 | 0.9424 | 0.5714 | 0.9266 | 0.8421 | 0.9048 | 0.9615 | 0.7931 | 0.9556 | 0.9492 | 0.8667 | 0.8790 | 0.7692 | 0.7949 | 0.8889 | 0.8718 | 0.7273 | 0.0 | 0.9849 | 0.9355 | 0.9231 |
| 0.1245 | 4.72 | 3000 | 0.3914 | 0.9150 | 0.8231 | 0.9472 | 0.6667 | 0.9259 | 0.8421 | 0.9 | 0.9615 | 0.8 | 0.9556 | 0.9655 | 0.8621 | 0.8924 | 0.7143 | 0.8101 | 0.75 | 0.8889 | 0.7500 | 0.0 | 0.9880 | 0.9180 | 0.9231 |
### Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
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