QuBERTa-finetuned-pos – AI Model by millawell | AlphaNeural AI

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millawell/
QuBERTa-finetuned-pos

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token-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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QuBERTa-finetuned-pos

This model is a fine-tuned version of Llamacha/QuBERTa on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.4249
Precision: 0.8372
Recall: 0.8702
F1: 0.8534
Accuracy: 0.8623

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
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	152	0.6146	0.6876	0.7482	0.7167	0.7360
No log	2.0	304	0.4937	0.7554	0.8041	0.7790	0.7932
No log	3.0	456	0.4525	0.7920	0.8238	0.8076	0.8200
0.5624	4.0	608	0.4294	0.8144	0.8426	0.8283	0.8391
0.5624	5.0	760	0.4245	0.8192	0.8521	0.8353	0.8445
0.5624	6.0	912	0.4357	0.8201	0.8607	0.8399	0.8480
0.3064	7.0	1064	0.4240	0.8308	0.8694	0.8497	0.8582
0.3064	8.0	1216	0.4231	0.8406	0.8757	0.8578	0.8653
0.3064	9.0	1368	0.4202	0.8389	0.8686	0.8535	0.8617
0.2227	10.0	1520	0.4249	0.8372	0.8702	0.8534	0.8623

Framework versions

Transformers 4.17.0
Pytorch 1.10.0+cu111
Datasets 2.0.0
Tokenizers 0.11.6