RISE_NER4 – AI Model by mappelgren | AlphaNeural AI

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mappelgren/
RISE_NER4

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token-classification

generated_from_trainer

distilbert/distilbert-base-uncased

autotrain_compatible

endpoints_compatible

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RISE_NER4

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

Loss: 0.1028
Precision: 0.8937
Recall: 0.9059
F1: 0.8997
Accuracy: 0.9834

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: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.012	1.0	16410	0.0639	0.8898	0.8978	0.8938	0.9827
0.0074	2.0	32820	0.0788	0.9006	0.8950	0.8978	0.9831
0.003	3.0	49230	0.0930	0.8938	0.9012	0.8975	0.9831
0.0014	4.0	65640	0.1028	0.8937	0.9059	0.8997	0.9834

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0