arg_extraction – AI Model by nattkorat | AlphaNeural AI

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nattkorat/
arg_extraction

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token-classification

generated_from_trainer

FacebookAI/xlm-roberta-base

autotrain_compatible

endpoints_compatible

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arg_extraction

This model is a fine-tuned version of xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.2267
Accuracy: 0.9342
Precision: 0.2597
Recall: 0.3414
F1: 0.2950

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: 32
eval_batch_size: 32
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
No log	1.0	201	0.3144	0.9226	0.2013	0.2680	0.2299
No log	2.0	402	0.2580	0.9307	0.2323	0.2962	0.2604
0.3942	3.0	603	0.2391	0.9320	0.2481	0.3355	0.2853
0.3942	4.0	804	0.2332	0.9321	0.2556	0.3463	0.2941
0.227	5.0	1005	0.2267	0.9342	0.2597	0.3414	0.2950

Framework versions

Transformers 4.52.4
Pytorch 2.7.1+cu126
Datasets 4.0.0
Tokenizers 0.21.1