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: 0.918525703200776
          - name: Recall
            type: recall
            value: 0.9458964541368403
          - name: F1
            type: f1
            value: 0.9320101697695399
          - name: Accuracy
            type: accuracy
            value: 0.9639352869753552

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: 0.2229
Precision: 0.9185
Recall: 0.9459
F1: 0.9320
Accuracy: 0.9639

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	363	0.1242	0.9164	0.9469	0.9314	0.9696
0.1307	2.0	726	0.1413	0.9197	0.9424	0.9309	0.9664
0.0831	3.0	1089	0.1366	0.9237	0.9477	0.9356	0.9682
0.0831	4.0	1452	0.1360	0.9283	0.9489	0.9385	0.9696
0.0646	5.0	1815	0.1572	0.9171	0.9427	0.9297	0.9646
0.0473	6.0	2178	0.1674	0.9069	0.9454	0.9257	0.9646
0.0367	7.0	2541	0.1783	0.9155	0.9414	0.9283	0.9644
0.0367	8.0	2904	0.1823	0.9244	0.9442	0.9342	0.9656
0.029	9.0	3267	0.1815	0.9190	0.9444	0.9315	0.9655
0.0227	10.0	3630	0.1945	0.9084	0.9457	0.9267	0.9617
0.0227	11.0	3993	0.1962	0.9134	0.9442	0.9285	0.9635
0.0188	12.0	4356	0.1893	0.9203	0.9442	0.9321	0.9651
0.0134	13.0	4719	0.1982	0.9181	0.9441	0.9309	0.9650
0.0126	14.0	5082	0.1962	0.9162	0.9447	0.9303	0.9667
0.0126	15.0	5445	0.2112	0.9196	0.9446	0.9319	0.9642
0.0099	16.0	5808	0.2138	0.9165	0.9449	0.9305	0.9630
0.007	17.0	6171	0.2110	0.9208	0.9447	0.9326	0.9652
0.0075	18.0	6534	0.2216	0.9210	0.9452	0.9330	0.9641
0.0075	19.0	6897	0.2232	0.9191	0.9461	0.9324	0.9640
0.0062	20.0	7260	0.2229	0.9185	0.9459	0.9320	0.9639

Framework versions

Transformers 4.36.2
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.0