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Roberta_recovery – AI Model by adity12345 | AlphaNeural AI
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Roberta_recovery
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transformers
tensorboard
safetensors
xlm-roberta
text-classification
generated_from_trainer
FacebookAI/xlm-roberta-base
finetune
mit
autotrain_compatible
endpoints_compatible
us
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Roberta_recovery
This model is a fine-tuned version of
FacebookAI/xlm-roberta-base
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.4683
Accuracy: 0.837
Auc: 0.846
Precision: 0.864
Recall: 0.907
F1: 0.885
F1-macro: 0.804
F1-micro: 0.837
F1-weighted: 0.835
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: 2e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 2
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
Auc
Precision
Recall
F1
F1-macro
F1-micro
F1-weighted
0.5564
1.1124
50
0.4683
0.837
0.846
0.864
0.907
0.885
0.804
0.837
0.835
Framework versions
Transformers 4.55.2
Pytorch 2.8.0+cu126
Datasets 4.0.0
Tokenizers 0.21.4
