metadata

license: mit
base_model: neuralmind/bert-base-portuguese-cased
tags:
  - generated_from_trainer
datasets:
  - harem
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: NER_harem_bert-base-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: 0.6852879944482998
          - name: Recall
            type: recall
            value: 0.7377661561449383
          - name: F1
            type: f1
            value: 0.7105594531390537
          - name: Accuracy
            type: accuracy
            value: 0.952219112355058

NER_harem_bert-base-portuguese-cased

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

Loss: 0.2351
Precision: 0.6853
Recall: 0.7378
F1: 0.7106
Accuracy: 0.9522

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	0.7692	0.0	0.0	0.0	0.8358
No log	2.0	32	0.4831	0.3140	0.2731	0.2921	0.8790
No log	3.0	48	0.3405	0.4692	0.4897	0.4793	0.9119
No log	4.0	64	0.2747	0.5481	0.6156	0.5799	0.9340
No log	5.0	80	0.2282	0.6077	0.6758	0.6399	0.9443
No log	6.0	96	0.2145	0.6267	0.6892	0.6565	0.9479
No log	7.0	112	0.2223	0.6395	0.6926	0.6650	0.9493
No log	8.0	128	0.2100	0.6822	0.7378	0.7089	0.9530
No log	9.0	144	0.2077	0.6810	0.7497	0.7137	0.9537
No log	10.0	160	0.2173	0.6846	0.7460	0.7140	0.9523
No log	11.0	176	0.2226	0.7001	0.7594	0.7285	0.9542
No log	12.0	192	0.2204	0.7015	0.7568	0.7281	0.9538
No log	13.0	208	0.2278	0.6746	0.7411	0.7063	0.9533
No log	14.0	224	0.2351	0.6853	0.7378	0.7106	0.9522

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2