readme: ---
  license: cc-by-sa-4.0
  tags:
  - generated_from_trainer
  datasets:
  - lex_glue
  model-index:
  - name: ECHR_test_2_task_B
    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. -->
  
  # ECHR_test_2_task_B
  
  This model is a fine-tuned version of [nlpaueb/legal-bert-base-uncased](https://huggingface.co/nlpaueb/legal-bert-base-uncased) on the lex_glue dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2092
  - Macro-f1: 0.5250
  - Micro-f1: 0.6190
  
  ## 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: 3e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 10
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Macro-f1 | Micro-f1 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
  | 0.2119        | 0.44  | 500   | 0.2945          | 0.2637   | 0.4453   |
  | 0.1702        | 0.89  | 1000  | 0.2734          | 0.3246   | 0.4843   |
  | 0.1736        | 1.33  | 1500  | 0.2633          | 0.3725   | 0.5133   |
  | 0.1571        | 1.78  | 2000  | 0.2549          | 0.3942   | 0.5417   |
  | 0.1476        | 2.22  | 2500  | 0.2348          | 0.4187   | 0.5649   |
  | 0.1599        | 2.67  | 3000  | 0.2427          | 0.4286   | 0.5606   |
  | 0.1481        | 3.11  | 3500  | 0.2210          | 0.4664   | 0.5780   |
  | 0.1412        | 3.56  | 4000  | 0.2542          | 0.4362   | 0.5617   |
  | 0.1505        | 4.0   | 4500  | 0.2249          | 0.4728   | 0.5863   |
  | 0.1425        | 4.44  | 5000  | 0.2311          | 0.4576   | 0.5845   |
  | 0.1461        | 4.89  | 5500  | 0.2261          | 0.4590   | 0.5832   |
  | 0.1451        | 5.33  | 6000  | 0.2248          | 0.4738   | 0.5901   |
  | 0.1281        | 5.78  | 6500  | 0.2317          | 0.4641   | 0.5896   |
  | 0.1354        | 6.22  | 7000  | 0.2366          | 0.4639   | 0.5946   |
  | 0.1204        | 6.67  | 7500  | 0.2311          | 0.4875   | 0.5877   |
  | 0.1229        | 7.11  | 8000  | 0.2083          | 0.4815   | 0.6020   |
  | 0.1368        | 7.56  | 8500  | 0.2170          | 0.5213   | 0.6021   |
  | 0.1288        | 8.0   | 9000  | 0.2136          | 0.5336   | 0.6176   |
  | 0.1275        | 8.44  | 9500  | 0.2180          | 0.5204   | 0.6082   |
  | 0.1232        | 8.89  | 10000 | 0.2147          | 0.5334   | 0.6083   |
  | 0.1319        | 9.33  | 10500 | 0.2121          | 0.5312   | 0.6186   |
  | 0.1267        | 9.78  | 11000 | 0.2092          | 0.5250   | 0.6190   |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.11.0+cu113
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  