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outputs – AI Model by ISEGURA | AlphaNeural AI
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transformers
pytorch
roberta
text-classification
generated_from_trainer
mit
autotrain_compatible
endpoints_compatible
us
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This model is a fine-tuned version of
roberta-base
on the None dataset. It achieves the following results on the evaluation set:
Loss: 1.5411
Accuracy: 0.5226
F1: 0.4181
Precision: 0.4320
Recall: 0.4102
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: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
F1
Precision
Recall
1.5774
1.0
638
1.3539
0.5329
0.3936
0.4611
0.4001
1.0496
2.0
1276
1.4186
0.5226
0.3943
0.4234
0.3883
0.7299
3.0
1914
1.5411
0.5226
0.4181
0.4320
0.4102
Framework versions
Transformers 4.25.1
Pytorch 1.13.1+cu116
Datasets 2.8.0
Tokenizers 0.13.2
