readme: ---
  license: cc-by-sa-4.0
  tags:
  - generated_from_trainer
  datasets:
  - lex_glue
  model-index:
  - name: ECHR_test_2
    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 A
  
  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: 0.1998
  - Macro-f1: 0.5295
  - Micro-f1: 0.6157
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
  | 0.2142        | 0.44  | 500   | 0.2887          | 0.2391   | 0.4263   |
  | 0.172         | 0.89  | 1000  | 0.2672          | 0.2908   | 0.4628   |
  | 0.1737        | 1.33  | 1500  | 0.2612          | 0.3657   | 0.5102   |
  | 0.1581        | 1.78  | 2000  | 0.2412          | 0.3958   | 0.5468   |
  | 0.1509        | 2.22  | 2500  | 0.2264          | 0.3950   | 0.5552   |
  | 0.1606        | 2.67  | 3000  | 0.2342          | 0.4006   | 0.5511   |
  | 0.1491        | 3.11  | 3500  | 0.2176          | 0.4558   | 0.5622   |
  | 0.1392        | 3.56  | 4000  | 0.2454          | 0.4128   | 0.5596   |
  | 0.15          | 4.0   | 4500  | 0.2113          | 0.4684   | 0.5874   |
  | 0.1461        | 4.44  | 5000  | 0.2179          | 0.4631   | 0.5815   |
  | 0.1457        | 4.89  | 5500  | 0.2151          | 0.4805   | 0.5949   |
  | 0.1443        | 5.33  | 6000  | 0.2155          | 0.5123   | 0.5917   |
  | 0.1279        | 5.78  | 6500  | 0.2131          | 0.4915   | 0.5998   |
  | 0.1377        | 6.22  | 7000  | 0.2244          | 0.4705   | 0.5944   |
  | 0.1242        | 6.67  | 7500  | 0.2150          | 0.5089   | 0.5918   |
  | 0.1222        | 7.11  | 8000  | 0.2045          | 0.4801   | 0.5981   |
  | 0.1372        | 7.56  | 8500  | 0.2074          | 0.5317   | 0.5962   |
  | 0.1289        | 8.0   | 9000  | 0.2035          | 0.5323   | 0.6126   |
  | 0.1295        | 8.44  | 9500  | 0.2058          | 0.5213   | 0.6073   |
  | 0.123         | 8.89  | 10000 | 0.2027          | 0.5486   | 0.6135   |
  | 0.1335        | 9.33  | 10500 | 0.1984          | 0.5442   | 0.6249   |
  | 0.1258        | 9.78  | 11000 | 0.1998          | 0.5295   | 0.6157   |
  
  
  ### Framework versions
  
  - Transformers 4.19.2
  - Pytorch 1.11.0+cu113
  - Datasets 2.2.2
  - Tokenizers 0.12.1
  