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BiBert-Classification – AI Model by HCKLab | AlphaNeural AI
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BiBert-Classification
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transformers
pytorch
tensorboard
bert
text-classification
generated_from_trainer
mit
autotrain_compatible
endpoints_compatible
us
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BiBert-Classification
This model is a fine-tuned version of
nlptown/bert-base-multilingual-uncased-sentiment
on the None dataset. It achieves the following results on the evaluation set:
Loss: 1.0853
Accuracy: 0.7433
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: 5
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
1.0981
1.0
9718
1.1034
0.7328
1.0394
2.0
19436
1.0853
0.7433
0.9649
3.0
29154
1.1041
0.7362
0.8884
4.0
38872
1.1618
0.7315
0.8005
5.0
48590
1.2340
0.7251
Framework versions
Transformers 4.21.1
Pytorch 1.12.1+cu113
Datasets 2.4.0
Tokenizers 0.12.1
