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EmotiNet – AI Model by Priyanshuchaudhary2425 | AlphaNeural AI
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Priyanshuchaudhary2425
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EmotiNet
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transformers
tensorboard
safetensors
bert
text-classification
generated_from_trainer
google-bert/bert-base-cased
finetune
apache-2.0
autotrain_compatible
endpoints_compatible
us
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Model card
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EmotiNet
This model is a fine-tuned version of
bert-base-cased
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 0.3224
Accuracy: 0.9242
Precision: 0.8830
Recall: 0.8990
F1: 0.8902
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: 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
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
Precision
Recall
F1
0.1494
1.0
1500
0.3036
0.9237
0.8783
0.9166
0.8921
0.1481
2.0
3000
0.2944
0.9242
0.8893
0.8867
0.8877
0.072
3.0
4500
0.3224
0.9242
0.8830
0.8990
0.8902
Framework versions
Transformers 4.38.1
Pytorch 2.1.0+cu121
Tokenizers 0.15.2
