Loss: 0.3180
Loc: {'precision': 0.8, 'recall': 0.8695652173913043, 'f1': 0.8333333333333333, 'number': 23}
Misc: {'precision': 0.8620689655172413, 'recall': 0.78125, 'f1': 0.8196721311475409, 'number': 32}
Org: {'precision': 0.6923076923076923, 'recall': 0.8709677419354839, 'f1': 0.7714285714285716, 'number': 31}
Per: {'precision': 0.9375, 'recall': 0.8823529411764706, 'f1': 0.9090909090909091, 'number': 17}
Overall Precision: 0.7982
Overall Recall: 0.8447
Overall F1: 0.8208
Overall Accuracy: 0.9332

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: 8

Training results

Training Loss	Epoch	Step	Validation Loss	Loc	Misc	Org	Per	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
No log	1.0	22	1.2089	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 23}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 32}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 31}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}	0.0	0.0	0.0	0.6115
No log	2.0	44	0.7922	{'precision': 0.28, 'recall': 0.30434782608695654, 'f1': 0.2916666666666667, 'number': 23}	{'precision': 0.7272727272727273, 'recall': 0.25, 'f1': 0.37209302325581395, 'number': 32}	{'precision': 0.3194444444444444, 'recall': 0.7419354838709677, 'f1': 0.4466019417475728, 'number': 31}	{'precision': 0.4, 'recall': 0.23529411764705882, 'f1': 0.29629629629629634, 'number': 17}	0.3559	0.4078	0.3801	0.7375
No log	3.0	66	0.4308	{'precision': 0.6785714285714286, 'recall': 0.8260869565217391, 'f1': 0.7450980392156864, 'number': 23}	{'precision': 0.625, 'recall': 0.625, 'f1': 0.625, 'number': 32}	{'precision': 0.4444444444444444, 'recall': 0.6451612903225806, 'f1': 0.5263157894736842, 'number': 31}	{'precision': 0.9375, 'recall': 0.8823529411764706, 'f1': 0.9090909090909091, 'number': 17}	0.6116	0.7184	0.6607	0.8892
No log	4.0	88	0.2770	{'precision': 0.7692307692307693, 'recall': 0.8695652173913043, 'f1': 0.8163265306122449, 'number': 23}	{'precision': 0.7586206896551724, 'recall': 0.6875, 'f1': 0.7213114754098361, 'number': 32}	{'precision': 0.5, 'recall': 0.7419354838709677, 'f1': 0.5974025974025974, 'number': 31}	{'precision': 0.9411764705882353, 'recall': 0.9411764705882353, 'f1': 0.9411764705882353, 'number': 17}	0.6864	0.7864	0.7330	0.9165
No log	5.0	110	0.2744	{'precision': 0.7142857142857143, 'recall': 0.8695652173913043, 'f1': 0.7843137254901961, 'number': 23}	{'precision': 0.6486486486486487, 'recall': 0.75, 'f1': 0.6956521739130435, 'number': 32}	{'precision': 0.5813953488372093, 'recall': 0.8064516129032258, 'f1': 0.6756756756756758, 'number': 31}	{'precision': 0.9411764705882353, 'recall': 0.9411764705882353, 'f1': 0.9411764705882353, 'number': 17}	0.68	0.8252	0.7456	0.9165
No log	6.0	132	0.3121	{'precision': 0.8, 'recall': 0.8695652173913043, 'f1': 0.8333333333333333, 'number': 23}	{'precision': 0.8333333333333334, 'recall': 0.78125, 'f1': 0.8064516129032259, 'number': 32}	{'precision': 0.6829268292682927, 'recall': 0.9032258064516129, 'f1': 0.7777777777777778, 'number': 31}	{'precision': 0.9375, 'recall': 0.8823529411764706, 'f1': 0.9090909090909091, 'number': 17}	0.7857	0.8544	0.8186	0.9272
No log	7.0	154	0.3132	{'precision': 0.8, 'recall': 0.8695652173913043, 'f1': 0.8333333333333333, 'number': 23}	{'precision': 0.8571428571428571, 'recall': 0.75, 'f1': 0.7999999999999999, 'number': 32}	{'precision': 0.675, 'recall': 0.8709677419354839, 'f1': 0.7605633802816901, 'number': 31}	{'precision': 0.9375, 'recall': 0.8823529411764706, 'f1': 0.9090909090909091, 'number': 17}	0.7890	0.8350	0.8113	0.9302
No log	8.0	176	0.3180	{'precision': 0.8, 'recall': 0.8695652173913043, 'f1': 0.8333333333333333, 'number': 23}	{'precision': 0.8620689655172413, 'recall': 0.78125, 'f1': 0.8196721311475409, 'number': 32}	{'precision': 0.6923076923076923, 'recall': 0.8709677419354839, 'f1': 0.7714285714285716, 'number': 31}	{'precision': 0.9375, 'recall': 0.8823529411764706, 'f1': 0.9090909090909091, 'number': 17}	0.7982	0.8447	0.8208	0.9332

Framework versions

Transformers 4.46.3
Pytorch 2.5.1+cu121
Datasets 3.1.0
Tokenizers 0.20.3

SweetMask/roberta-finetuned-ner-vi

roberta-finetuned-ner-vi

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

SweetMask/
roberta-finetuned-ner-vi