roberta-finetuned-ner-without-data-sort – AI Model by kSaluja | AlphaNeural AI

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kSaluja/
roberta-finetuned-ner-without-data-sort

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token-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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roberta-finetuned-ner-without-data-sort

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

Loss: 0.0420
Precision: 0.9914
Recall: 0.9909
F1: 0.9912
Accuracy: 0.9920

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: 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	Precision	Recall	F1	Accuracy
No log	1.0	213	0.1879	0.9378	0.9414	0.9396	0.9493
No log	2.0	426	0.1038	0.9725	0.9750	0.9737	0.9751
0.4424	3.0	639	0.0701	0.9861	0.9851	0.9856	0.9863
0.4424	4.0	852	0.0637	0.9882	0.9880	0.9881	0.9880
0.0675	5.0	1065	0.0546	0.9851	0.9878	0.9865	0.9879
0.0675	6.0	1278	0.0480	0.9894	0.9904	0.9899	0.9901
0.0675	7.0	1491	0.0473	0.9919	0.9904	0.9912	0.9911
0.0426	8.0	1704	0.0441	0.9921	0.9916	0.9919	0.9921
0.0426	9.0	1917	0.0426	0.9921	0.9916	0.9919	0.9922
0.033	10.0	2130	0.0420	0.9914	0.9909	0.9912	0.9920

Framework versions

Transformers 4.17.0
Pytorch 1.10.0+cu111
Datasets 2.0.0
Tokenizers 0.11.6