XLM-R-BASE-Finetune-step2-finetune-and-eval-may31-easy-deluge-3-D-06-01-T-05-31 – AI Model by haturusinghe | AlphaNeural AI

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haturusinghe/
XLM-R-BASE-Finetune-step2-finetune-and-eval-may31-easy-deluge-3-D-06-01-T-05-31

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text-classification

generated_from_trainer

FacebookAI/xlm-roberta-base

autotrain_compatible

endpoints_compatible

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XLM-R-BASE-Finetune-step2-finetune-and-eval-may31-easy-deluge-3-D-06-01-T-05-31

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: 0.3977
Precision 0: 0.8721
Precision 1: 0.8029
Recall 0: 0.8633
Recall 1: 0.8148
F1 0: 0.8677
F1 1: 0.8088
Precision Weighted: 0.8440
Recall Weighted: 0.8436
F1 Weighted: 0.8438
Accuracy: 0.8436
F1 Macro: 0.8382

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: 16
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_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
0.5501	1.0	469	0.4524	0.7699	0.8995	0.9556	0.5823	0.8528	0.7069	0.8226	0.804	0.7936	0.804	0.7799
0.3869	2.0	938	0.4545	0.8995	0.7229	0.7710	0.8739	0.8303	0.7913	0.8278	0.8128	0.8145	0.8128	0.8108
0.3825	3.0	1407	0.3678	0.8429	0.8191	0.8855	0.7586	0.8637	0.7877	0.8333	0.834	0.8329	0.834	0.8257
0.2683	4.0	1876	0.3977	0.8721	0.8029	0.8633	0.8148	0.8677	0.8088	0.8440	0.8436	0.8438	0.8436	0.8382
0.2297	5.0	2345	0.5155	0.8711	0.7937	0.8552	0.8148	0.8631	0.8041	0.8396	0.8388	0.8391	0.8388	0.8336

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1