fm-tc-end_mix_xml_labelsrt – AI Model by adriansanz | AlphaNeural AI

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adriansanz/
fm-tc-end_mix_xml_labelsrt

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text-classification

generated_from_trainer

FacebookAI/xlm-roberta-base

autotrain_compatible

endpoints_compatible

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fm-tc-end_mix_xml

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

Loss: 0.2061
Accuracy: 0.97
Precision: 0.9706
Recall: 0.9700
F1: 0.9700

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: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.6642	1.0	1188	0.4540	0.894	0.8983	0.8940	0.8938
0.4074	2.0	2376	0.4206	0.918	0.9241	0.9180	0.9183
0.2403	3.0	3564	0.4380	0.918	0.9221	0.9180	0.9165
0.1625	4.0	4752	0.4773	0.926	0.9297	0.9260	0.9262
0.0991	5.0	5940	0.2999	0.952	0.9536	0.9520	0.9521
0.0549	6.0	7128	0.2217	0.966	0.9671	0.966	0.9659
0.0226	7.0	8316	0.2770	0.964	0.9650	0.9640	0.9637
0.0154	8.0	9504	0.2061	0.97	0.9706	0.9700	0.9700
0.0061	9.0	10692	0.2372	0.97	0.9706	0.9700	0.9699
0.0048	10.0	11880	0.2419	0.964	0.9650	0.9640	0.9640

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.19.2
Tokenizers 0.19.1