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MiniLMv2-L12-H384-distilled-finetuned-spam-detection – AI Model by Rhuax | AlphaNeural AI
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Rhuax
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MiniLMv2-L12-H384-distilled-finetuned-spam-detection
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transformers
pytorch
tensorboard
roberta
text-classification
generated_from_trainer
sms_spam
model-index
autotrain_compatible
endpoints_compatible
us
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MiniLMv2-L12-H384-distilled-finetuned-spam-detection
This model is a fine-tuned version of
nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large
on the sms_spam dataset. It achieves the following results on the evaluation set:
Loss: 0.0938
Accuracy: 0.9928
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: 0.0001
train_batch_size: 32
eval_batch_size: 32
seed: 33
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 6
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
1.4101
1.0
131
0.4930
0.9763
0.8003
2.0
262
0.3999
0.9799
0.377
3.0
393
0.3196
0.9828
0.302
4.0
524
0.3462
0.9828
0.1945
5.0
655
0.1094
0.9928
0.1393
6.0
786
0.0938
0.9928
Framework versions
Transformers 4.17.0
Pytorch 1.10.2+cu113
Datasets 1.18.4
Tokenizers 0.12.1
