xlm-r-large_baseline_for_subasa – AI Model by haturusinghe | AlphaNeural AI

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haturusinghe/
xlm-r-large_baseline_for_subasa

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text-classification

generated_from_trainer

FacebookAI/xlm-roberta-large

autotrain_compatible

endpoints_compatible

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xlm_r_large-baseline_model-v2-fallen-oath-3

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

Loss: 0.5036
Precision 0: 0.8766
Precision 1: 0.7911
Recall 0: 0.8512
Recall 1: 0.8246
F1 0: 0.8637
F1 1: 0.8075
Precision Weighted: 0.8419
Recall Weighted: 0.8404
F1 Weighted: 0.8409
F1 Macro: 0.8356

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: 16
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	F1 Macro
0.4937	1.0	469	0.4268	0.8346	0.7933	0.8667	0.7488	0.8503	0.7704	0.8179	0.8188	0.8179	0.8104
0.3945	2.0	938	0.3987	0.9083	0.7168	0.7603	0.8877	0.8277	0.7931	0.8305	0.812	0.8137	0.8104
0.3721	3.0	1407	0.3612	0.8654	0.7992	0.8620	0.8039	0.8637	0.8016	0.8386	0.8384	0.8385	0.8326
0.2721	4.0	1876	0.4191	0.8514	0.8246	0.8875	0.7734	0.8691	0.7982	0.8405	0.8412	0.8403	0.8336
0.2144	5.0	2345	0.5036	0.8766	0.7911	0.8512	0.8246	0.8637	0.8075	0.8419	0.8404	0.8409	0.8356

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.19.2
Tokenizers 0.19.1