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Harm_detection – AI Model by DaJulster | AlphaNeural AI
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DaJulster
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Harm_detection
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transformers
tensorboard
safetensors
distilbert
text-classification
generated_from_trainer
distilbert/distilbert-base-uncased
finetune
apache-2.0
autotrain_compatible
endpoints_compatible
us
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Harm_detection
This model is a fine-tuned version of
distilbert/distilbert-base-uncased
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.1006
Accuracy: 0.9672
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: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
No log
1.0
223
0.0982
0.9659
No log
2.0
446
0.1006
0.9672
Framework versions
Transformers 4.38.2
Pytorch 2.1.2
Datasets 2.1.0
Tokenizers 0.15.2
