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haturusinghe/XLM-R-BASE-Finetune-step2-finetune-and-eval-may31-deft-sea-5-D-06-01-T-07-56

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Model Card

XLM-R-BASE-Finetune-step2-finetune-and-eval-may31-deft-sea-5-D-06-01-T-07-56

This model is a fine-tuned version of xlm-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:

—Loss: 0.4138
—Precision 0: 0.8686
—Precision 1: 0.7973
—Recall 0: 0.8593
—Recall 1: 0.8099
—F1 0: 0.8639
—F1 1: 0.8035
—Precision Weighted: 0.8397
—Recall Weighted: 0.8392
—F1 Weighted: 0.8394
—Accuracy: 0.8392
—F1 Macro: 0.8337

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
—trainbatchsize: 32
—evalbatchsize: 64
—seed: 42
—optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
—lrschedulertype: linear
—lrschedulerwarmup_ratio: 0.1
—num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision 0	Precision 1	Recall 0	Recall 1	F1 0	F1 1	Precision Weighted	Recall Weighted	F1 Weighted	Accuracy	F1 Macro
0.5706	1.0	235	0.4284	0.8281	0.7829	0.8599	0.7389	0.8437	0.7603	0.8098	0.8108	0.8098	0.8108	0.8020
0.4239	2.0	470	0.3919	0.7991	0.8685	0.9320	0.6571	0.8604	0.7482	0.8273	0.8204	0.8149	0.8204	0.8043
0.3552	3.0	705	0.3774	0.8168	0.8416	0.9098	0.7015	0.8608	0.7652	0.8269	0.8252	0.8220	0.8252	0.8130
0.292	4.0	940	0.4014	0.8627	0.7998	0.8633	0.7990	0.8630	0.7994	0.8372	0.8372	0.8372	0.8372	0.8312
0.2404	5.0	1175	0.4138	0.8686	0.7973	0.8593	0.8099	0.8639	0.8035	0.8397	0.8392	0.8394	0.8392	0.8337

Framework versions

—Transformers 4.41.2
—Pytorch 2.3.0+cu121
—Datasets 2.19.1
—Tokenizers 0.19.1