metadata

license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: distilbert-base-uncased-finetuned-ner
    results: []

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: 0.1040
Precision: 0.9760
Recall: 0.9707
F1: 0.9733
Accuracy: 0.9825

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: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.1497	1.0	4211	0.2500	0.8891	0.9142	0.9015	0.9319
0.0931	2.0	8422	0.1863	0.9296	0.9457	0.9376	0.9566
0.0583	3.0	12633	0.1546	0.9531	0.9490	0.9510	0.9658
0.035	4.0	16844	0.1834	0.9503	0.9544	0.9523	0.9628
0.0235	5.0	21055	0.1341	0.9528	0.9642	0.9584	0.9735
0.0161	6.0	25266	0.1647	0.9565	0.9544	0.9554	0.9687
0.0144	7.0	29477	0.1024	0.9694	0.9620	0.9657	0.9807
0.0116	8.0	33688	0.1290	0.9630	0.9620	0.9625	0.9769
0.0067	9.0	37899	0.1020	0.9716	0.9663	0.9690	0.9800
0.0042	10.0	42110	0.1298	0.9547	0.9620	0.9584	0.9728
0.0045	11.0	46321	0.1398	0.9675	0.9685	0.9680	0.9800
0.0024	12.0	50532	0.1176	0.9707	0.9707	0.9707	0.9789
0.0024	13.0	54743	0.0995	0.9717	0.9696	0.9707	0.9823
0.0011	14.0	58954	0.1071	0.9749	0.9685	0.9717	0.9818
0.0015	15.0	63165	0.1040	0.9760	0.9707	0.9733	0.9825

Framework versions

Transformers 4.28.0
Pytorch 2.0.1
Datasets 2.12.0
Tokenizers 0.13.3