metadata

license: mit
base_model: neuralmind/bert-large-portuguese-cased
tags:
  - generated_from_trainer
datasets:
  - harem
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: NER_harem_bert-large-portuguese-cased
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: harem
          type: harem
          config: default
          split: test
          args: default
        metrics:
          - name: Precision
            type: precision
            value: 0.7077353867693384
          - name: Recall
            type: recall
            value: 0.7553231228987672
          - name: F1
            type: f1
            value: 0.7307553306830503
          - name: Accuracy
            type: accuracy
            value: 0.9551379448220711

NER_harem_bert-large-portuguese-cased

This model is a fine-tuned version of neuralmind/bert-large-portuguese-cased on the harem dataset. It achieves the following results on the evaluation set:

Loss: 0.2487
Precision: 0.7077
Recall: 0.7553
F1: 0.7308
Accuracy: 0.9551

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: 3e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 300

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	16	0.6334	0.0163	0.0078	0.0106	0.8468
No log	2.0	32	0.4537	0.2614	0.3112	0.2841	0.8826
No log	3.0	48	0.3117	0.5262	0.5671	0.5458	0.9231
No log	4.0	64	0.2421	0.5852	0.6631	0.6217	0.9385
No log	5.0	80	0.2099	0.5950	0.6855	0.6370	0.9479
No log	6.0	96	0.2153	0.6810	0.7464	0.7122	0.9551
No log	7.0	112	0.2270	0.6894	0.7198	0.7043	0.9546
No log	8.0	128	0.2213	0.6918	0.7437	0.7168	0.9554
No log	9.0	144	0.2299	0.7021	0.7564	0.7283	0.9545
No log	10.0	160	0.2256	0.7002	0.7591	0.7284	0.9562
No log	11.0	176	0.2169	0.7100	0.7736	0.7404	0.9568
No log	12.0	192	0.2266	0.6981	0.7740	0.7341	0.9571
No log	13.0	208	0.2322	0.7093	0.7620	0.7347	0.9570
No log	14.0	224	0.2487	0.7077	0.7553	0.7308	0.9551

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2