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RuleBert-v0.3-k3 – AI Model by ribesstefano | AlphaNeural AI
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ribesstefano
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RuleBert-v0.3-k3
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transformers
tensorboard
safetensors
xlm-roberta
text-classification
Italian
legal ruling
generated_from_trainer
papluca/xlm-roberta-base-language-detection
finetune
mit
autotrain_compatible
endpoints_compatible
us
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ribesstefano/RuleBert-v0.3-k3
This model is a fine-tuned version of
papluca/xlm-roberta-base-language-detection
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.3332
F1: 0.4507
Roc Auc: 0.6503
Accuracy: 0.0714
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: 5e-06
train_batch_size: 2
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
training_steps: 8000
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
F1
Roc Auc
Accuracy
0.4445
0.06
250
0.4053
0.4824
0.6769
0.0
0.3665
0.12
500
0.3428
0.4528
0.6516
0.0714
0.3587
0.18
750
0.3332
0.4507
0.6503
0.0714
Framework versions
Transformers 4.36.2
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.0
