metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - lextreme
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: distilroberta-base-mapa_coarse-ner
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: lextreme
          type: lextreme
          config: mapa_coarse
          split: test
          args: mapa_coarse
        metrics:
          - name: Precision
            type: precision
            value: 0.7440758293838863
          - name: Recall
            type: recall
            value: 0.5805042016806723
          - name: F1
            type: f1
            value: 0.652190332326284
          - name: Accuracy
            type: accuracy
            value: 0.9871584939520047

distilroberta-base-mapa_coarse-ner

This model is a fine-tuned version of distilroberta-base on the lextreme dataset. It achieves the following results on the evaluation set:

Loss: 0.1020
Precision: 0.7441
Recall: 0.5805
F1: 0.6522
Accuracy: 0.9872

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
0.0343	1.0	1739	0.0694	0.6342	0.5205	0.5718	0.9841
0.0263	2.0	3478	0.0705	0.7961	0.5235	0.6317	0.9865
0.0183	3.0	5217	0.0670	0.7417	0.5313	0.6191	0.9864
0.015	4.0	6956	0.0632	0.7237	0.5850	0.6470	0.9869
0.0137	5.0	8695	0.0663	0.7311	0.6064	0.6629	0.9872
0.011	6.0	10434	0.0703	0.7163	0.5877	0.6457	0.9868
0.0096	7.0	12173	0.0799	0.7511	0.5676	0.6466	0.9871
0.0071	8.0	13912	0.0770	0.7386	0.5640	0.6396	0.9868
0.0068	9.0	15651	0.0827	0.7285	0.5674	0.6379	0.9868
0.0057	10.0	17390	0.0897	0.7611	0.5719	0.6531	0.9872
0.0053	11.0	19129	0.0940	0.7614	0.5627	0.6471	0.9871
0.004	12.0	20868	0.0874	0.7184	0.6084	0.6588	0.9873
0.0035	13.0	22607	0.0986	0.7513	0.5766	0.6525	0.9872
0.003	14.0	24346	0.1012	0.7396	0.5805	0.6505	0.9871
0.0026	15.0	26085	0.1020	0.7441	0.5805	0.6522	0.9872

Framework versions

Transformers 4.26.0
Pytorch 1.13.1+cu117
Datasets 2.9.0
Tokenizers 0.13.2