distilbert-base-uncased-finetuned-ner – AI Model by chandc | AlphaNeural AI

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chandc/
distilbert-base-uncased-finetuned-ner

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token-classification

generated_from_trainer

distilbert/distilbert-base-uncased

autotrain_compatible

endpoints_compatible

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distilbert-base-uncased-finetuned-ner

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: 0.2228
Precision: 0.8030
Recall: 0.8093
F1: 0.8061
Accuracy: 0.9545

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: 0.0005
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
lr_scheduler_warmup_ratio: 0.06
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.3032	1.0	878	0.3241	0.6979	0.5912	0.6401	0.9168
0.2666	2.0	1756	0.2822	0.6475	0.6577	0.6525	0.9221
0.2025	3.0	2634	0.2402	0.7021	0.7273	0.7144	0.9369
0.1421	4.0	3512	0.2158	0.7283	0.7331	0.7307	0.9390
0.111	5.0	4390	0.2189	0.7442	0.7395	0.7418	0.9417
0.0813	6.0	5268	0.2196	0.7307	0.7812	0.7551	0.9442
0.0538	7.0	6146	0.2169	0.7594	0.8049	0.7815	0.9497
0.0389	8.0	7024	0.2133	0.7929	0.7991	0.7960	0.9520
0.0263	9.0	7902	0.2192	0.8002	0.7991	0.7996	0.9530
0.0141	10.0	8780	0.2224	0.8029	0.8097	0.8063	0.9546

Framework versions

Transformers 4.37.2
Pytorch 2.2.0
Datasets 2.17.0
Tokenizers 0.15.2