xlnet-large-cased-ner-food-combined-weighted-v2 – AI Model by kanak8278 | AlphaNeural AI

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xlnet-large-cased-ner-food-combined-weighted-v2

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token-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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xlnet-large-cased-ner-food-combined-weighted-v2

This model is a fine-tuned version of xlnet-large-cased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.1182
Precision: 0.7436
Recall: 0.8947
F1: 0.8122
Accuracy: 0.9642

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-06
train_batch_size: 16
eval_batch_size: 24
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	Precision	Recall	F1	Accuracy
0.5366	1.12	500	0.1525	0.6775	0.8493	0.7537	0.9550
0.1697	2.25	1000	0.1385	0.6403	0.8580	0.7333	0.9457
0.1279	3.37	1500	0.1340	0.7899	0.8768	0.8311	0.9693
0.1178	4.49	2000	0.1247	0.7750	0.8876	0.8275	0.9679
0.1021	5.62	2500	0.1182	0.7436	0.8947	0.8122	0.9642
0.0957	6.74	3000	0.1192	0.7344	0.8876	0.8038	0.9626
0.0882	7.87	3500	0.1226	0.7641	0.8901	0.8223	0.9667
0.0802	8.99	4000	0.1323	0.7872	0.8901	0.8355	0.9695

Framework versions

Transformers 4.27.4
Pytorch 2.0.0+cu118
Datasets 2.11.0
Tokenizers 0.13.3