metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - token_classification_v2
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: favs_token_classification_v2_updated_data
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: token_classification_v2
          type: token_classification_v2
          config: default
          split: train
          args: default
        metrics:
          - name: Precision
            type: precision
            value: 0.6923076923076923
          - name: Recall
            type: recall
            value: 0.8357142857142857
          - name: F1
            type: f1
            value: 0.7572815533980584
          - name: Accuracy
            type: accuracy
            value: 0.8493150684931506

favs_token_classification_v2_updated_data

This model is a fine-tuned version of bert-base-cased on the token_classification_v2 dataset. It achieves the following results on the evaluation set:

Loss: 0.5346
Precision: 0.6923
Recall: 0.8357
F1: 0.7573
Accuracy: 0.8493

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: 1.5e-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: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
2.3096	1.0	13	1.9927	0.3011	0.2	0.2403	0.3726
2.038	2.0	26	1.7093	0.2569	0.2643	0.2606	0.4274
1.8391	3.0	39	1.4452	0.3057	0.4214	0.3544	0.5562
1.4912	4.0	52	1.2176	0.4130	0.5429	0.4691	0.6493
1.3296	5.0	65	1.0368	0.4973	0.6643	0.5688	0.7123
1.2036	6.0	78	0.9084	0.5053	0.6786	0.5793	0.7260
0.9244	7.0	91	0.8148	0.5543	0.7286	0.6296	0.7616
0.8293	8.0	104	0.7482	0.5698	0.7286	0.6395	0.7726
0.7422	9.0	117	0.6961	0.5833	0.75	0.6562	0.7836
0.6379	10.0	130	0.6613	0.6124	0.7786	0.6855	0.8027
0.6071	11.0	143	0.6357	0.6193	0.7786	0.6899	0.8082
0.5526	12.0	156	0.6033	0.6433	0.7857	0.7074	0.8164
0.537	13.0	169	0.5813	0.6512	0.8	0.7179	0.8301
0.4806	14.0	182	0.5706	0.6608	0.8071	0.7267	0.8329
0.4503	15.0	195	0.5594	0.6647	0.8071	0.7290	0.8356
0.4149	16.0	208	0.5503	0.6805	0.8214	0.7443	0.8438
0.4175	17.0	221	0.5430	0.6824	0.8286	0.7484	0.8438
0.4337	18.0	234	0.5396	0.6923	0.8357	0.7573	0.8493
0.3965	19.0	247	0.5361	0.6882	0.8357	0.7548	0.8493
0.3822	20.0	260	0.5346	0.6923	0.8357	0.7573	0.8493

Framework versions

Transformers 4.21.1
Pytorch 1.12.1
Datasets 2.4.0
Tokenizers 0.12.1