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justification_classifier_model – AI Model by DotsinPritam | AlphaNeural AI
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justification_classifier_model
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transformers
safetensors
modernbert
text-classification
generated_from_trainer
answerdotai/ModernBERT-base
finetune
apache-2.0
text-embeddings-inference
endpoints_compatible
us
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justification_classifier_model
This model is a fine-tuned version of
answerdotai/ModernBERT-base
on the None dataset. It achieves the following results on the evaluation set:
Loss: 1.1738
Accuracy: 0.5714
F1: 0.5678
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: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 3
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
F1
No log
1.0
35
1.3060
0.4429
0.4122
No log
2.0
70
1.1103
0.5571
0.5329
No log
3.0
105
1.1738
0.5714
0.5678
Framework versions
Transformers 4.51.3
Pytorch 2.6.0+cpu
Datasets 3.1.0
Tokenizers 0.21.1
