metadata

license: agpl-3.0
tags:
  - generated_from_trainer
datasets:
  - mim_gold_ner
metrics:
  - precision
  - recall
  - f1
  - accuracy
widget:
  - text: >-
      Bónus feðgarnir Jóhannes Jónsson og Jón Ásgeir Jóhannesson opnuðu fyrstu
      Bónusbúðina í 400 fermetra húsnæði við Skútuvog laugardaginn 8. apríl 1989
model-index:
  - name: XLMR-ENIS-finetuned-ner
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: mim_gold_ner
          type: mim_gold_ner
          args: mim-gold-ner
        metrics:
          - name: Precision
            type: precision
            value: 0.861851332398317
          - name: Recall
            type: recall
            value: 0.8384309266628767
          - name: F1
            type: f1
            value: 0.849979828251974
          - name: Accuracy
            type: accuracy
            value: 0.9830620929487668

XLMR-ENIS-finetuned-ner

This model is a fine-tuned version of vesteinn/XLMR-ENIS on the mim_gold_ner dataset. It achieves the following results on the evaluation set:

Loss: 0.0938
Precision: 0.8619
Recall: 0.8384
F1: 0.8500
Accuracy: 0.9831

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: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0574	1.0	2904	0.0983	0.8374	0.8061	0.8215	0.9795
0.0321	2.0	5808	0.0991	0.8525	0.8235	0.8378	0.9811
0.0179	3.0	8712	0.0938	0.8619	0.8384	0.8500	0.9831

Framework versions

Transformers 4.11.2
Pytorch 1.9.0+cu102
Datasets 1.12.1
Tokenizers 0.10.3