Loss: 0.1100
0: {'precision': 0.95, 'recall': 0.926829268292683, 'f1-score': 0.9382716049382716, 'support': 41.0}
1: {'precision': 0.9451476793248945, 'recall': 0.9696969696969697, 'f1-score': 0.9572649572649573, 'support': 231.0}
2: {'precision': 0.9872650090964221, 'recall': 0.9878640776699029, 'f1-score': 0.9875644525326054, 'support': 1648.0}
3: {'precision': 0.978021978021978, 'recall': 0.9621621621621622, 'f1-score': 0.9700272479564033, 'support': 370.0}
Accuracy: 0.9808
Macro avg: {'precision': 0.9651086666108237, 'recall': 0.9616381194554294, 'f1-score': 0.9632820656730594, 'support': 2290.0}
Weighted avg: {'precision': 0.9808558868048411, 'recall': 0.9807860262008734, 'f1-score': 0.9807919827285054, 'support': 2290.0}

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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	0	1	2	3	Accuracy	Macro avg	Weighted avg
0.2357	1.0	1145	0.2453	{'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 0.0}	{'precision': 0.9367088607594937, 'recall': 0.8951612903225806, 'f1-score': 0.9154639175257732, 'support': 248.0}	{'precision': 0.9842328684050939, 'recall': 0.9603550295857988, 'f1-score': 0.97214734950584, 'support': 1690.0}	{'precision': 0.9340659340659341, 'recall': 0.9659090909090909, 'f1-score': 0.9497206703910615, 'support': 352.0}	0.9541	{'precision': 0.7137519158076304, 'recall': 0.7053563527043676, 'f1-score': 0.7093329843556686, 'support': 2290.0}	{'precision': 0.9713749143511669, 'recall': 0.9541484716157205, 'f1-score': 0.9625614620912293, 'support': 2290.0}
0.1043	2.0	2290	0.1176	{'precision': 0.925, 'recall': 0.9736842105263158, 'f1-score': 0.9487179487179487, 'support': 38.0}	{'precision': 0.9409282700421941, 'recall': 0.9780701754385965, 'f1-score': 0.9591397849462365, 'support': 228.0}	{'precision': 0.9939357186173439, 'recall': 0.9761762954139369, 'f1-score': 0.9849759615384616, 'support': 1679.0}	{'precision': 0.9285714285714286, 'recall': 0.9797101449275363, 'f1-score': 0.9534555712270804, 'support': 345.0}	0.9769	{'precision': 0.9471088543077417, 'recall': 0.9769102065765964, 'f1-score': 0.9615723166074318, 'support': 2290.0}	{'precision': 0.9776667510852766, 'recall': 0.9768558951965065, 'f1-score': 0.9770532596137309, 'support': 2290.0}
0.0506	3.0	3435	0.1100	{'precision': 0.95, 'recall': 0.926829268292683, 'f1-score': 0.9382716049382716, 'support': 41.0}	{'precision': 0.9451476793248945, 'recall': 0.9696969696969697, 'f1-score': 0.9572649572649573, 'support': 231.0}	{'precision': 0.9872650090964221, 'recall': 0.9878640776699029, 'f1-score': 0.9875644525326054, 'support': 1648.0}	{'precision': 0.978021978021978, 'recall': 0.9621621621621622, 'f1-score': 0.9700272479564033, 'support': 370.0}	0.9808	{'precision': 0.9651086666108237, 'recall': 0.9616381194554294, 'f1-score': 0.9632820656730594, 'support': 2290.0}	{'precision': 0.9808558868048411, 'recall': 0.9807860262008734, 'f1-score': 0.9807919827285054, 'support': 2290.0}

Framework versions

Transformers 4.46.2
Pytorch 2.5.0+cu121
Datasets 3.1.0
Tokenizers 0.20.3

sanchezmari/results

results

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

sanchezmari/
results