joelito/legal-slovak-roberta-base

Model

1

stars

7

commits

1

linked in READMEs

Feb 13, 2023

updated

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

README

legal-slovak-roberta-base

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

  • Loss: 0.5008

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: 16
  • eval_batch_size: 16
  • seed: 42
  • distributed_type: tpu
  • num_devices: 8
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 512
  • total_eval_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.05
  • training_steps: 200000

Training results

Training LossEpochStepValidation Loss
0.83653.03500000.6204
0.8166.061000000.5410
0.792110.011500000.5061
0.695513.042000000.5008

Framework versions

  • Transformers 4.20.1
  • Pytorch 1.12.0+cu102
  • Datasets 2.9.0
  • Tokenizers 0.12.0

Contributors

joelniklaus

7 commits

joelito/legal-slovak-roberta-base

Model

1

stars

7

commits

1

linked in READMEs

Feb 13, 2023

updated

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

README

legal-slovak-roberta-base

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

  • Loss: 0.5008

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: 16
  • eval_batch_size: 16
  • seed: 42
  • distributed_type: tpu
  • num_devices: 8
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 512
  • total_eval_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.05
  • training_steps: 200000

Training results

Training LossEpochStepValidation Loss
0.83653.03500000.6204
0.8166.061000000.5410
0.792110.011500000.5061
0.695513.042000000.5008

Framework versions

  • Transformers 4.20.1
  • Pytorch 1.12.0+cu102
  • Datasets 2.9.0
  • Tokenizers 0.12.0

Contributors

joelniklaus

7 commits