joelito/legal-german-roberta-base

Model

1

stars

24

commits

1

linked in READMEs

Jan 7, 2023

updated

endpoints_compatible
fill-mask
generated_from_trainer
pytorch
roberta
transformers
Browse cluster: Multilingual Legal NLP Models

README

legal-german-roberta-base

This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7080
  • Accuracy: 0.8387

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: 0.0001
  • train_batch_size: 1024
  • eval_batch_size: 512
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.05
  • training_steps: 1000000

Training results

Training LossEpochStepAccuracyValidation Loss
2.10080.05500000.65332.0523
1.52480.11000000.76611.1575
1.31520.151500000.76741.1281
1.12390.22000000.79710.9458
0.94720.252500000.78760.9979
0.9610.33000000.80750.8798
1.01790.353500000.80180.9102
1.0370.44000000.81950.8107
1.12060.454500000.81520.8323
1.08650.55000000.82420.7829
0.96160.555500000.82240.7895
0.77270.66000000.82850.7585
0.98711.046500000.83200.7391
1.06791.097000000.83110.7436
0.92031.147500000.83550.7187
0.96261.198000000.83530.7242
0.72631.248500000.70940.8378
0.85781.299000000.71400.8368
0.76931.349500000.70910.8377
1.04881.3910000000.70800.8387

Framework versions

  • Transformers 4.20.1
  • Pytorch 1.10.0+cu113
  • Datasets 2.8.0
  • Tokenizers 0.12.1

Contributors

joelniklaus

24 commits

joelito/legal-german-roberta-base

Model

1

stars

24

commits

1

linked in READMEs

Jan 7, 2023

updated

endpoints_compatible
fill-mask
generated_from_trainer
pytorch
roberta
transformers
Browse cluster: Multilingual Legal NLP Models

README

legal-german-roberta-base

This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7080
  • Accuracy: 0.8387

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: 0.0001
  • train_batch_size: 1024
  • eval_batch_size: 512
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.05
  • training_steps: 1000000

Training results

Training LossEpochStepAccuracyValidation Loss
2.10080.05500000.65332.0523
1.52480.11000000.76611.1575
1.31520.151500000.76741.1281
1.12390.22000000.79710.9458
0.94720.252500000.78760.9979
0.9610.33000000.80750.8798
1.01790.353500000.80180.9102
1.0370.44000000.81950.8107
1.12060.454500000.81520.8323
1.08650.55000000.82420.7829
0.96160.555500000.82240.7895
0.77270.66000000.82850.7585
0.98711.046500000.83200.7391
1.06791.097000000.83110.7436
0.92031.147500000.83550.7187
0.96261.198000000.83530.7242
0.72631.248500000.70940.8378
0.85781.299000000.71400.8368
0.76931.349500000.70910.8377
1.04881.3910000000.70800.8387

Framework versions

  • Transformers 4.20.1
  • Pytorch 1.10.0+cu113
  • Datasets 2.8.0
  • Tokenizers 0.12.1

Contributors

joelniklaus

24 commits