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Self_Efficacy_continuous – AI Model by ajrayman | AlphaNeural AI
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ajrayman
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Self_Efficacy_continuous
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transformers
safetensors
roberta
text-classification
generated_from_trainer
FacebookAI/roberta-base
finetune
mit
autotrain_compatible
endpoints_compatible
us
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Self_Efficacy_continuous
This model is a fine-tuned version of
roberta-base
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.0382
Rmse: 0.1955
Mae: 0.1564
Corr: 0.2817
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: 32
eval_batch_size: 32
seed: 1234
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.06
num_epochs: 8
Training results
Training Loss
Epoch
Step
Validation Loss
Rmse
Mae
Corr
No log
1.0
235
0.0367
0.1917
0.1459
0.2475
No log
2.0
470
0.0359
0.1896
0.1455
0.2784
0.0703
3.0
705
0.0440
0.2098
0.1572
0.2740
0.0703
4.0
940
0.0382
0.1955
0.1564
0.2817
Framework versions
Transformers 4.44.1
Pytorch 1.11.0
Datasets 2.12.0
Tokenizers 0.19.1
