xlmr-sold_mrp0.75_s13_pre_ep4
xlm-roberta-base Finetuning
Model Description
Base Model: xlm-roberta-base
Intermediate Task: mrp-
Pre-Finetuned Model: None
Training Information
Epochs: 4
Steps: 0
Validation Time: 0.01s
Performance Metrics
Main Metrics
Validation Loss: 0.058366
Validation Accuracy: 0.943280
Validation F1: 0.624220
Classification Report
{'0': {'precision': 0.9516352367137016, 'recall': 0.9900871235507992, 'f1-score': 0.9704804491813199, 'support': 29154.0}, '1': {'precision': 0.5390749601275917, 'recall': 0.18725761772853186, 'f1-score': 0.2779605263157895, 'support': 1805.0}, 'accuracy': 0.9432798216996673, 'macro avg': {'precision': 0.7453550984206467, 'recall': 0.5886723706396655, 'f1-score': 0.6242204877485547, 'support': 30959.0}, 'weighted avg': {'precision': 0.9275817692490572, 'recall': 0.9432798216996673, 'f1-score': 0.9301045177632417, 'support': 30959.0}}
Masked Metrics
Masked Accuracy: 0.980321
Masked F1: 0.916850
Masked Classification Report
{'0': {'precision': 0.9919200822682532, 'recall': 0.9870863992982798, 'f1-score': 0.9894973376972303, 'support': 20521.0}, '1': {'precision': 0.8146853146853147, 'recall': 0.8759398496240601, 'f1-score': 0.8442028985507246, 'support': 1330.0}, 'accuracy': 0.9803212667612466, 'macro avg': {'precision': 0.903302698476784, 'recall': 0.93151312446117, 'f1-score': 0.9168501181239774, 'support': 21851.0}, 'weighted avg': {'precision': 0.9811323727407577, 'recall': 0.9803212667612466, 'f1-score': 0.980653733099507, 'support': 21851.0}}
Trainer Arguments:
"learning_rate": 2e-05
"epochs": 5
"batch_size": 16
"model": xlm-roberta-base
"intermediate_task": mrp
"n_tk_label": 2
"mask_ratio": 0.75
"seed": 13
"dataset": sold
"finetuning_stage": pre
"val_int": 10000
"patience": 3
"skip_empty_rat": True