ner_model – AI Model by balciberin | AlphaNeural AI

You can deploy this model and start earning money today!

balciberin/
ner_model

like

0

token-classification

generated_from_trainer

distilbert/distilbert-base-uncased

autotrain_compatible

endpoints_compatible

Views

No views yet

ner_model

This model is a fine-tuned version of distilbert-base-uncased on the wnut_17 dataset. It achieves the following results on the evaluation set:

Loss: 0.3832
Precision: 0.5632
Recall: 0.4171
F1: 0.4792
Accuracy: 0.9479

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	425	0.2828	0.6021	0.3800	0.4659	0.9466
0.074	2.0	850	0.2955	0.5825	0.3892	0.4667	0.9474
0.0457	3.0	1275	0.3072	0.5857	0.4180	0.4878	0.9492
0.0234	4.0	1700	0.3430	0.5911	0.4059	0.4813	0.9481
0.0144	5.0	2125	0.3468	0.5406	0.4198	0.4726	0.9476
0.0107	6.0	2550	0.3742	0.5541	0.4032	0.4667	0.9470
0.0107	7.0	2975	0.3779	0.5861	0.4133	0.4848	0.9483
0.0081	8.0	3400	0.3802	0.5537	0.4013	0.4653	0.9477
0.0059	9.0	3825	0.3750	0.5511	0.4198	0.4766	0.9478
0.0033	10.0	4250	0.3832	0.5632	0.4171	0.4792	0.9479

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.19.2
Tokenizers 0.19.1