resume-ner – AI Model by KeerthiSubramani | AlphaNeural AI

You can deploy this model and start earning money today!

KeerthiSubramani/
resume-ner

like

0

token-classification

generated_from_trainer

distilbert/distilbert-base-uncased

endpoints_compatible

Views

No views yet

resume-ner

This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0147
F1: 0.9827
Precision: 0.9944
Recall: 0.9712

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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Precision	Recall
0.0303	1.0	100	0.0302	0.9789	0.9586	1.0
0.0193	2.0	200	0.0169	0.9843	1.0	0.9692
0.0178	3.0	300	0.0155	0.9843	1.0	0.9692
0.0137	4.0	400	0.0149	0.9843	1.0	0.9692
0.0150	5.0	500	0.0147	0.9827	0.9944	0.9712

Framework versions

Transformers 5.10.1
Pytorch 2.11.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2