joelito/legal-italian-roberta-base

Model

1

stars

23

commits

1

linked in READMEs

Jan 14, 2023

updated

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

README

legal-italian-roberta-base

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

  • Loss: 0.4799

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: 1000000

Training results

Training LossEpochStepValidation Loss
1.02480.05500000.8033
0.9120.11000000.6825
0.88531.01500000.6205
0.8471.052000000.5954
0.83951.12500000.5859
0.74852.013000000.5632
0.71542.063500000.5495
0.68512.114000000.5456
0.60743.014500000.5331
0.62963.065000000.5226
0.61253.115500000.5146
0.59834.026000000.5038
0.64714.076500000.4976
0.6334.127000000.4982
0.69175.027500000.4906
0.71785.078000000.4833
0.69885.128500000.4754
0.71356.029000000.4734
0.72696.079500000.4826
0.70856.1210000000.4799

Framework versions

  • Transformers 4.20.1
  • Pytorch 1.12.0+cu102
  • Datasets 2.8.0
  • Tokenizers 0.12.1

Contributors

joelniklaus

23 commits

joelito/legal-italian-roberta-base

Model

1

stars

23

commits

1

linked in READMEs

Jan 14, 2023

updated

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

README

legal-italian-roberta-base

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

  • Loss: 0.4799

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: 1000000

Training results

Training LossEpochStepValidation Loss
1.02480.05500000.8033
0.9120.11000000.6825
0.88531.01500000.6205
0.8471.052000000.5954
0.83951.12500000.5859
0.74852.013000000.5632
0.71542.063500000.5495
0.68512.114000000.5456
0.60743.014500000.5331
0.62963.065000000.5226
0.61253.115500000.5146
0.59834.026000000.5038
0.64714.076500000.4976
0.6334.127000000.4982
0.69175.027500000.4906
0.71785.078000000.4833
0.69885.128500000.4754
0.71356.029000000.4734
0.72696.079500000.4826
0.70856.1210000000.4799

Framework versions

  • Transformers 4.20.1
  • Pytorch 1.12.0+cu102
  • Datasets 2.8.0
  • Tokenizers 0.12.1

Contributors

joelniklaus

23 commits