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bert-base-uncased-finetuned-m_avoid_harm_seler – AI Model by Gregorig | AlphaNeural AI
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Gregorig
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bert-base-uncased-finetuned-m_avoid_harm_seler
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transformers
tensorboard
safetensors
bert
text-classification
generated_from_trainer
google-bert/bert-base-uncased
finetune
apache-2.0
autotrain_compatible
endpoints_compatible
us
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DeBERTa-sentiment
This model is a fine-tuned version of
bert-base-uncased
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 0.3547
Accuracy: 0.905
F1: 0.9262
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: 3
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
F1
0.6133
1.0
51
0.4752
0.755
0.8324
0.4678
2.0
102
0.3521
0.89
0.9182
0.3508
3.0
153
0.3547
0.905
0.9262
Framework versions
Transformers 4.41.2
Pytorch 2.3.0+cu121
Tokenizers 0.19.1
