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sentiment-bert-base-uncased – AI Model by thinhkosay | AlphaNeural AI
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thinhkosay
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sentiment-bert-base-uncased
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transformers
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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sentiment-bert-base-uncased
This model is a fine-tuned version of
google-bert/bert-base-uncased
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.3179
Precision: 0.8880
Recall: 0.8902
F1: 0.8891
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: 2
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3
Training results
Training Loss
Epoch
Step
Validation Loss
Precision
Recall
F1
0.3727
0.9990
512
0.3047
0.8582
0.8804
0.8546
0.2802
2.0
1025
0.3083
0.8914
0.8780
0.8837
0.1985
2.9971
1536
0.3179
0.8880
0.8902
0.8891
Framework versions
Transformers 4.40.2
Pytorch 2.2.1+cu121
Datasets 2.15.0
Tokenizers 0.19.1
