---
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
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# 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