xlmr-sold_mrp0.1_s13_pre_ep1
xlm-roberta-base Finetuning
Model Description
Base Model: xlm-roberta-base
Intermediate Task: mrp-
Pre-Finetuned Model: None
Training Information
Epochs: 1
Steps: 0
Validation Time: 0.01s
Performance Metrics
Main Metrics
Validation Loss: 0.145824
Validation Accuracy: 0.945993
Validation F1: 0.554689
Classification Report
{'0': {'precision': 0.9457600726659313, 'recall': 1.0, 'f1-score': 0.9721240413471157, 'support': 29154.0}, '1': {'precision': 1.0, 'recall': 0.07368421052631578, 'f1-score': 0.13725490196078433, 'support': 1805.0}, 'accuracy': 0.9459930876320295, 'macro avg': {'precision': 0.9728800363329657, 'recall': 0.5368421052631579, 'f1-score': 0.55468947165395, 'support': 30959.0}, 'weighted avg': {'precision': 0.9489224186344056, 'recall': 0.9459930876320295, 'f1-score': 0.9234487354072491, 'support': 30959.0}}
Masked Metrics
Masked Accuracy: 0.938267
Masked F1: 0.558061
Masked Classification Report
{'0': {'precision': 0.9379336931380108, 'recall': 1.0, 'f1-score': 0.9679729460911081, 'support': 2433.0}, '1': {'precision': 1.0, 'recall': 0.08, 'f1-score': 0.14814814814814814, 'support': 175.0}, 'accuracy': 0.9382668711656442, 'macro avg': {'precision': 0.9689668465690053, 'recall': 0.54, 'f1-score': 0.558060547119628, 'support': 2608.0}, 'weighted avg': {'precision': 0.942098418483428, 'recall': 0.9382668711656442, 'f1-score': 0.912961696229138, 'support': 2608.0}}
Trainer Arguments:
"learning_rate": 2e-05
"epochs": 5
"batch_size": 16
"model": xlm-roberta-base
"intermediate_task": mrp
"n_tk_label": 2
"mask_ratio": 0.1
"seed": 13
"dataset": sold
"finetuning_stage": pre
"val_int": 10000
"patience": 3
"skip_empty_rat": True