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emotion-classifier – AI Model by pheonixnrj | AlphaNeural AI
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pheonixnrj
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emotion-classifier
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transformers
pytorch
roberta
text-classification
generated_from_trainer
mit
autotrain_compatible
endpoints_compatible
us
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emotion-classifier
This model is a fine-tuned version of
roberta-base
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.2110
Accuracy: 0.9380
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: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 6
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
0.3083
1.0
670
0.2363
0.9296
0.1783
2.0
1340
0.2110
0.9380
0.1373
3.0
2010
0.2379
0.9407
0.0647
4.0
2680
0.2235
0.9519
0.0831
5.0
3350
0.2574
0.9407
0.0637
6.0
4020
0.2605
0.9463
Framework versions
Transformers 4.30.0
Pytorch 2.2.1+cu121
Datasets 2.19.0
Tokenizers 0.13.3
