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BiBert-Classification-1 – AI Model by HCKLab | AlphaNeural AI
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BiBert-Classification-1
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transformers
pytorch
tensorboard
bert
text-classification
generated_from_trainer
mit
text-embeddings-inference
endpoints_compatible
us
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BiBert-Classification-1
This model is a fine-tuned version of
nlptown/bert-base-multilingual-uncased-sentiment
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 0.7371
Accuracy: 0.7881
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
gradient_accumulation_steps: 4
total_train_batch_size: 64
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
0.7927
1.0
4001
0.7371
0.7881
0.7234
2.0
8002
0.7999
0.7667
0.673
3.0
12003
0.7956
0.7709
Framework versions
Transformers 4.24.0
Pytorch 1.12.1+cu113
Datasets 2.6.1
Tokenizers 0.13.1
