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benali – AI Model by KonradBRG | AlphaNeural AI
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KonradBRG
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benali
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transformers
safetensors
bert
text-classification
generated_from_trainer
google-bert/bert-base-multilingual-cased
finetune
apache-2.0
autotrain_compatible
endpoints_compatible
us
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benali
This model is a fine-tuned version of
bert-base-multilingual-cased
on the None dataset. It achieves the following results on the evaluation set:
Loss: 2.9977
Accuracy: 0.3359
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-05
train_batch_size: 64
eval_batch_size: 128
seed: 42
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: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 5
mixed_precision_training: Native AMP
label_smoothing_factor: 0.1
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
2.9745
1.0
8096
3.0077
0.3168
2.8419
2.0
16192
2.9562
0.3306
2.6782
3.0
24288
2.9254
0.3396
2.4903
4.0
32384
2.9492
0.3388
2.3638
5.0
40480
2.9977
0.3359
Framework versions
Transformers 4.56.0
Pytorch 2.8.0+cu128
Datasets 4.0.0
Tokenizers 0.22.0
