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Bert-Phrasebank-Sentiment-Analysis – AI Model by pkbiswas | AlphaNeural AI
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pkbiswas
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Bert-Phrasebank-Sentiment-Analysis
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transformers
pytorch
bert
text-classification
generated_from_trainer
financial_phrasebank
google-bert/bert-base-uncased
finetune
apache-2.0
model-index
autotrain_compatible
endpoints_compatible
us
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phrasebank-sentiment-analysis
This model is a fine-tuned version of
bert-base-uncased
on the financial_phrasebank dataset. It achieves the following results on the evaluation set:
Loss: 0.5194
F1: 0.8447
Accuracy: 0.8618
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: 32
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 4
Training results
Training Loss
Epoch
Step
Validation Loss
F1
Accuracy
0.597
0.94
100
0.3968
0.8181
0.8439
0.2739
1.89
200
0.3645
0.8409
0.8576
0.1375
2.83
300
0.5283
0.8282
0.8453
0.0646
3.77
400
0.5194
0.8447
0.8618
Framework versions
Transformers 4.34.1
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1
