readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: cross-en-de-roberta-sentence-transformer-openlegal
    results: []
  ---
  
  --- Part of BA Thesis by Enis Settouf @ HTW Berlin Business computing ---
  
  This model was trained additionally on a MLM task for the legal domain on German laws and legal Cases
  
  Data Source: [OpenLegalData.io](https://de.openlegaldata.io/pages/api/)
  
  The rest of this model card has been generated automatically:
  
  
  <!-- 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. -->
  
  # cross-en-de-roberta-sentence-transformer-openlegal
  
  This model is a fine-tuned version of [T-Systems-onsite/cross-en-de-roberta-sentence-transformer](https://huggingface.co/T-Systems-onsite/cross-en-de-roberta-sentence-transformer) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.3120
  
  ## 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: 8
  - eval_batch_size: 8
  - seed: 42
  - distributed_type: tpu
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3.0
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss |
  |:-------------:|:-----:|:-----:|:---------------:|
  | 6.4835        | 0.06  | 500   | 5.2259          |
  | 4.9569        | 0.13  | 1000  | 4.5163          |
  | 4.4158        | 0.19  | 1500  | 4.1257          |
  | 4.1221        | 0.25  | 2000  | 3.8695          |
  | 3.8853        | 0.32  | 2500  | 3.6586          |
  | 3.7092        | 0.38  | 3000  | 3.5126          |
  | 3.5779        | 0.45  | 3500  | 3.3889          |
  | 3.4424        | 0.51  | 4000  | 3.2731          |
  | 3.3556        | 0.57  | 4500  | 3.1984          |
  | 3.2627        | 0.64  | 5000  | 3.1103          |
  | 3.1855        | 0.7   | 5500  | 3.0306          |
  | 3.1381        | 0.76  | 6000  | 2.9796          |
  | 3.0763        | 0.83  | 6500  | 2.9299          |
  | 2.9985        | 0.89  | 7000  | 2.8740          |
  | 2.9359        | 0.95  | 7500  | 2.8300          |
  | 2.8954        | 1.02  | 8000  | 2.7861          |
  | 2.8322        | 1.08  | 8500  | 2.7450          |
  | 2.816         | 1.14  | 9000  | 2.7255          |
  | 2.8013        | 1.21  | 9500  | 2.6872          |
  | 2.7414        | 1.27  | 10000 | 2.6538          |
  | 2.707         | 1.34  | 10500 | 2.6284          |
  | 2.6866        | 1.4   | 11000 | 2.6021          |
  | 2.6429        | 1.46  | 11500 | 2.5721          |
  | 2.6269        | 1.53  | 12000 | 2.5646          |
  | 2.6173        | 1.59  | 12500 | 2.5323          |
  | 2.5959        | 1.65  | 13000 | 2.5052          |
  | 2.5692        | 1.72  | 13500 | 2.4993          |
  | 2.5563        | 1.78  | 14000 | 2.4840          |
  | 2.5448        | 1.84  | 14500 | 2.4635          |
  | 2.4932        | 1.91  | 15000 | 2.4581          |
  | 2.5106        | 1.97  | 15500 | 2.4342          |
  | 2.5009        | 2.03  | 16000 | 2.4260          |
  | 2.46          | 2.1   | 16500 | 2.4152          |
  | 2.4417        | 2.16  | 17000 | 2.4079          |
  | 2.4568        | 2.23  | 17500 | 2.4010          |
  | 2.442         | 2.29  | 18000 | 2.3875          |
  | 2.4328        | 2.35  | 18500 | 2.3724          |
  | 2.4126        | 2.42  | 19000 | 2.3645          |
  | 2.4063        | 2.48  | 19500 | 2.3612          |
  | 2.362         | 2.54  | 20000 | 2.3565          |
  | 2.3877        | 2.61  | 20500 | 2.3507          |
  | 2.3839        | 2.67  | 21000 | 2.3353          |
  | 2.3657        | 2.73  | 21500 | 2.3326          |
  | 2.3464        | 2.8   | 22000 | 2.3262          |
  | 2.3915        | 2.86  | 22500 | 2.3259          |
  | 2.3613        | 2.93  | 23000 | 2.3195          |
  | 2.358         | 2.99  | 23500 | 2.3165          |
  
  
  ### Framework versions
  
  - Transformers 4.21.2
  - Pytorch 1.8.2+cpu
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  