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finetuning-sentiment-analysis-en – AI Model by viviastaari | AlphaNeural AI
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viviastaari
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finetuning-sentiment-analysis-en
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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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finetuning-sentiment-analysis-en
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: 0.0792
Accuracy: 0.9803
F1: 0.9856
Precision: 0.9875
Recall: 0.9837
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: 5e-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: 3.0
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
F1
Precision
Recall
0.426
1.0
1408
0.2718
0.8910
0.9201
0.9251
0.9151
0.3247
2.0
2816
0.1552
0.9540
0.9665
0.9656
0.9674
0.1582
3.0
4224
0.0792
0.9803
0.9856
0.9875
0.9837
Framework versions
Transformers 4.19.2
Pytorch 1.11.0+cu113
Datasets 2.2.2
Tokenizers 0.12.1
