metadata

tags:
  - generated_from_trainer
datasets:
  - wikiann
metrics:
  - precision
  - recall
  - f1
  - accuracy
language:
  - sr
model_index:
  - name: distilbert-srb-ner
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: wikiann
          type: wikiann
          args: sr
        metric:
          name: Accuracy
          type: accuracy
          value: 0.9576561462374611

distilbert-srb-ner

This model was trained from scratch on the wikiann dataset. It achieves the following results on the evaluation set:

Loss: 0.2972
Precision: 0.8871
Recall: 0.9100
F1: 0.8984
Accuracy: 0.9577

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: 32
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.3818	1.0	625	0.2175	0.8175	0.8370	0.8272	0.9306
0.198	2.0	1250	0.1766	0.8551	0.8732	0.8640	0.9458
0.1423	3.0	1875	0.1702	0.8597	0.8763	0.8679	0.9473
0.079	4.0	2500	0.1774	0.8674	0.8875	0.8773	0.9515
0.0531	5.0	3125	0.2011	0.8688	0.8965	0.8825	0.9522
0.0429	6.0	3750	0.2082	0.8769	0.8970	0.8868	0.9538
0.032	7.0	4375	0.2268	0.8764	0.8916	0.8839	0.9528
0.0204	8.0	5000	0.2423	0.8726	0.8959	0.8841	0.9529
0.0148	9.0	5625	0.2522	0.8774	0.8991	0.8881	0.9538
0.0125	10.0	6250	0.2544	0.8823	0.9024	0.8922	0.9559
0.0108	11.0	6875	0.2592	0.8780	0.9041	0.8909	0.9553
0.007	12.0	7500	0.2672	0.8877	0.9056	0.8965	0.9571
0.0048	13.0	8125	0.2714	0.8879	0.9089	0.8982	0.9583
0.0049	14.0	8750	0.2872	0.8873	0.9068	0.8970	0.9573
0.0034	15.0	9375	0.2915	0.8883	0.9114	0.8997	0.9577
0.0027	16.0	10000	0.2890	0.8865	0.9103	0.8983	0.9581
0.0028	17.0	10625	0.2885	0.8877	0.9085	0.8980	0.9576
0.0014	18.0	11250	0.2928	0.8860	0.9073	0.8965	0.9577
0.0013	19.0	11875	0.2963	0.8856	0.9099	0.8976	0.9576
0.001	20.0	12500	0.2972	0.8871	0.9100	0.8984	0.9577

Framework versions

Transformers 4.9.2
Pytorch 1.9.0
Datasets 1.11.0
Tokenizers 0.10.1