---
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
---

<!-- 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-lenerbr-finetuned-ner

This model is a fine-tuned version of [pierreguillou/ner-bert-large-cased-pt-lenerbr](https://huggingface.co/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