metadata

tags:
  - generated_from_trainer
datasets:
  - wikiann
metrics:
  - precision
  - recall
  - f1
  - accuracy
inference: false
language:
  - sk
model-index:
  - name: bertoslav-limited-ner
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: wikiann sk
          type: wikiann
          args: sk
        metrics:
          - name: Precision
            type: precision
            value: 0.8985571260306242
          - name: Recall
            type: recall
            value: 0.9173994738819993
          - name: F1
            type: f1
            value: 0.9078805459481573
          - name: Accuracy
            type: accuracy
            value: 0.9700235061239639

Named Entity Recognition based on bertoslav-limited

This model is a fine-tuned version of crabz/bertoslav-limited on the Slovak wikiann dataset. It achieves the following results on the evaluation set:

Loss: 0.2119
Precision: 0.8986
Recall: 0.9174
F1: 0.9079
Accuracy: 0.9700

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 24
eval_batch_size: 24
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10.0

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.2953	1.0	834	0.1516	0.8413	0.8647	0.8529	0.9549
0.0975	2.0	1668	0.1304	0.8787	0.9056	0.8920	0.9658
0.0487	3.0	2502	0.1405	0.8916	0.8958	0.8937	0.9660
0.025	4.0	3336	0.1658	0.8850	0.9116	0.8981	0.9669
0.0161	5.0	4170	0.1739	0.8974	0.9127	0.9050	0.9693
0.0074	6.0	5004	0.1888	0.8900	0.9144	0.9020	0.9687
0.0051	7.0	5838	0.1996	0.8946	0.9145	0.9044	0.9693
0.0039	8.0	6672	0.2052	0.8993	0.9158	0.9075	0.9697
0.0024	9.0	7506	0.2112	0.8946	0.9171	0.9057	0.9696
0.0018	10.0	8340	0.2119	0.8986	0.9174	0.9079	0.9700

Framework versions

Transformers 4.14.0.dev0
Pytorch 1.10.0
Datasets 1.16.1
Tokenizers 0.10.3