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bert_combo – AI Model by amalik27 | AlphaNeural AI
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amalik27
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bert_combo
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transformers
pytorch
tensorboard
bert
text-classification
generated_from_trainer
apache-2.0
autotrain_compatible
endpoints_compatible
us
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bert_combo
This model is a fine-tuned version of
bert-base-uncased
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.0881
Accuracy: 0.9862
F1: 0.9862
Precision: 0.9788
Recall: 0.9940
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: 1e-05
train_batch_size: 8
eval_batch_size: 8
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
0.0848
1.0
6059
0.0705
0.9834
0.9834
0.9903
0.9766
0.0363
2.0
12118
0.0925
0.9821
0.9821
0.9701
0.9950
0.0118
3.0
18177
0.0881
0.9862
0.9862
0.9788
0.9940
Framework versions
Transformers 4.27.4
Pytorch 2.0.0+cu118
Datasets 2.11.0
Tokenizers 0.13.3
