joelito/legal-french-roberta-base

Model

0

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-french-roberta-base

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

  • Loss: 0.4293

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
0.86490.05500000.7819
0.78520.11000000.6027
0.58981.021500000.5842
0.61361.072000000.5343
0.61351.122500000.5461
0.58042.033000000.5295
0.56022.083500000.5120
0.54462.134000000.4904
0.54143.054500000.4853
0.57653.15000000.4788
0.69034.015500000.4597
0.61494.066000000.4556
0.56494.116500000.4543
0.64495.037000000.4489
0.64255.087500000.4386
0.62635.138000000.4344
0.60356.058500000.4317
0.6076.19000000.4332
0.58997.019500000.4321
0.57517.0610000000.4293

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-french-roberta-base

Model

0

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-french-roberta-base

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

  • Loss: 0.4293

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
0.86490.05500000.7819
0.78520.11000000.6027
0.58981.021500000.5842
0.61361.072000000.5343
0.61351.122500000.5461
0.58042.033000000.5295
0.56022.083500000.5120
0.54462.134000000.4904
0.54143.054500000.4853
0.57653.15000000.4788
0.69034.015500000.4597
0.61494.066000000.4556
0.56494.116500000.4543
0.64495.037000000.4489
0.64255.087500000.4386
0.62635.138000000.4344
0.60356.058500000.4317
0.6076.19000000.4332
0.58997.019500000.4321
0.57517.0610000000.4293

Framework versions

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

Contributors

joelniklaus

23 commits