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scenescope-mood – AI Model by RedMinder56 | AlphaNeural AI
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RedMinder56
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scenescope-mood
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transformers
safetensors
roberta
text-classification
generated_from_trainer
FacebookAI/roberta-base
finetune
mit
endpoints_compatible
us
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scenescope-mood
This model is a fine-tuned version of
roberta-base
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.8130
Accuracy: 0.6667
F1: 0.6675
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: 16
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: linear
lr_scheduler_warmup_steps: 0.1
num_epochs: 5
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
F1
0.7078
1.0
70
0.6921
0.5167
0.3520
0.7055
2.0
140
0.6720
0.5333
0.3881
0.6339
3.0
210
0.5943
0.7333
0.7182
0.4387
4.0
280
0.5529
0.725
0.7241
0.3427
5.0
350
0.5633
0.7667
0.7667
Framework versions
Transformers 5.0.0
Pytorch 2.10.0+cpu
Datasets 4.0.0
Tokenizers 0.22.2
