metadata

base_model: pierreguillou/ner-bert-large-cased-pt-lenerbr
tags:
  - generated_from_trainer
datasets:
  - contratos_tceal
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: ner-bert-large-cased-pt-lenerbr-finetuned-ner
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: contratos_tceal
          type: contratos_tceal
          config: contratos_tceal
          split: validation
          args: contratos_tceal
        metrics:
          - name: Precision
            type: precision
            value: 0.7549019607843137
          - name: Recall
            type: recall
            value: 0.8115313081215128
          - name: F1
            type: f1
            value: 0.7821930086644756
          - name: Accuracy
            type: accuracy
            value: 0.883160638230246

ner-bert-large-cased-pt-lenerbr-finetuned-ner

This model is a fine-tuned version of pierreguillou/ner-bert-large-cased-pt-lenerbr on the contratos_tceal dataset. It achieves the following results on the evaluation set:

Loss: nan
Precision: 0.7549
Recall: 0.8115
F1: 0.7822
Accuracy: 0.8832

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: 4
eval_batch_size: 4
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	91	nan	0.6987	0.7433	0.7203	0.8620
No log	2.0	182	nan	0.7040	0.7564	0.7292	0.8624
No log	3.0	273	nan	0.7317	0.7929	0.7611	0.8731
No log	4.0	364	nan	0.7501	0.8097	0.7788	0.8838
No log	5.0	455	nan	0.7504	0.8332	0.7897	0.8857
0.3495	6.0	546	nan	0.7551	0.8103	0.7817	0.8799
0.3495	7.0	637	nan	0.7533	0.8215	0.7859	0.8824
0.3495	8.0	728	nan	0.7578	0.7991	0.7779	0.8785
0.3495	9.0	819	nan	0.7520	0.8196	0.7843	0.8840
0.3495	10.0	910	nan	0.7549	0.8115	0.7822	0.8832

Framework versions

Transformers 4.36.0
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0