                    precision    recall  f1-score   support
      
             anger       0.75      0.73      0.74       919
      anticipation       0.59      0.37      0.46       321
           disgust       0.53      0.40      0.46       423
              fear       0.77      0.62      0.69       298
               joy       0.84      0.82      0.83       873
              love       0.76      0.56      0.64       245
          optimism       0.53      0.36      0.43       278
         pessimism       0.53      0.41      0.46       495
           sadness       0.71      0.67      0.69       644
          surprise       0.44      0.21      0.29       122
             trust       0.49      0.20      0.29       122
      
         micro avg       0.70      0.59      0.64      4740
         macro avg       0.63      0.49      0.54      4740
      weighted avg       0.68      0.59      0.63      4740
       samples avg       0.66      0.61      0.60      4740

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 0.1
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	F1 Macro
No log	1.0	223	0.2726	0.4179
No log	2.0	446	0.2680	0.4866
0.2574	3.0	669	0.3498	0.5016

Framework versions

Transformers 5.3.0
Pytorch 2.10.0+cu128
Datasets 4.7.0
Tokenizers 0.22.2

Jeanievas/finetuned_model_emotion_detection

finetuned_model_emotion_detection

Model description

Training and evaluation data

Metrics

Training hyperparameters

Training results

Framework versions

Jeanievas/
finetuned_model_emotion_detection