metadata

tags:
  - generated_from_trainer
datasets:
  - contratos_tceal
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: ner-bert-large-cased-pt-contratos_tceal
    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: 0.9134177215189874
          - name: Recall
            type: recall
            value: 0.9168996188055909
          - name: F1
            type: f1
            value: 0.9151553582752061
          - name: Accuracy
            type: accuracy
            value: 0.9556322655972385

ner-bert-large-cased-pt-contratos_tceal

This model was trained from scratch on the contratos_tceal dataset. It achieves the following results on the evaluation set:

Loss: 0.3141
Precision: 0.9134
Recall: 0.9169
F1: 0.9152
Accuracy: 0.9556

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: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	252	0.2193	0.9026	0.8948	0.8987	0.9488
0.2496	2.0	504	0.2110	0.8957	0.9098	0.9027	0.9494
0.2496	3.0	756	0.2098	0.9166	0.9105	0.9136	0.9531
0.1666	4.0	1008	0.2063	0.9221	0.9146	0.9183	0.9559
0.1666	5.0	1260	0.2165	0.9219	0.9146	0.9182	0.9562
0.1255	6.0	1512	0.2143	0.9175	0.9133	0.9154	0.9555
0.1255	7.0	1764	0.2278	0.9181	0.9146	0.9164	0.9559
0.092	8.0	2016	0.2404	0.9188	0.9174	0.9181	0.9561
0.092	9.0	2268	0.2538	0.9133	0.9100	0.9117	0.9533
0.069	10.0	2520	0.2654	0.9132	0.9118	0.9125	0.9543
0.069	11.0	2772	0.2796	0.9085	0.9133	0.9109	0.9527
0.0498	12.0	3024	0.2827	0.9130	0.9149	0.9139	0.9552
0.0498	13.0	3276	0.2869	0.9127	0.9144	0.9135	0.9557
0.0397	14.0	3528	0.2993	0.9123	0.9093	0.9108	0.9546
0.0397	15.0	3780	0.2951	0.9056	0.9144	0.9100	0.9547
0.0312	16.0	4032	0.2989	0.9092	0.9136	0.9114	0.9566
0.0312	17.0	4284	0.3104	0.9115	0.9113	0.9114	0.9554
0.0257	18.0	4536	0.3098	0.9143	0.9161	0.9152	0.9564
0.0257	19.0	4788	0.3129	0.9141	0.9166	0.9154	0.9556
0.0207	20.0	5040	0.3141	0.9134	0.9169	0.9152	0.9556

Framework versions

Transformers 4.36.2
Pytorch 2.1.0+cu121
Datasets 2.16.0
Tokenizers 0.15.0