metadata

license: apache-2.0
base_model: bert-base-cased
tags:
  - generated_from_trainer
datasets:
  - lener_br
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: bert-base-cased-finetuned-ner
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: lener_br
          type: lener_br
          config: lener_br
          split: validation
          args: lener_br
        metrics:
          - name: Precision
            type: precision
            value: 0.7640519805855644
          - name: Recall
            type: recall
            value: 0.818242790073776
          - name: F1
            type: f1
            value: 0.7902194154319487
          - name: Accuracy
            type: accuracy
            value: 0.9615441099339138

bert-base-cased-finetuned-ner

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

Loss: nan
Precision: 0.7641
Recall: 0.8182
F1: 0.7902
Accuracy: 0.9615

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: 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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	432	nan	0.6807	0.7773	0.7258	0.9450
0.3019	2.0	864	nan	0.7244	0.7725	0.7476	0.9531
0.0871	3.0	1296	nan	0.7352	0.8192	0.7749	0.9571
0.0527	4.0	1728	nan	0.7455	0.7864	0.7654	0.9557
0.031	5.0	2160	nan	0.7334	0.7976	0.7642	0.9544
0.0223	6.0	2592	nan	0.7703	0.8343	0.8010	0.9624
0.0171	7.0	3024	nan	0.7279	0.8119	0.7676	0.9569
0.0171	8.0	3456	nan	0.7609	0.8067	0.7831	0.9613
0.012	9.0	3888	nan	0.7585	0.8152	0.7858	0.9608
0.0097	10.0	4320	nan	0.7641	0.8182	0.7902	0.9615

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0