---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-large-uncased
tags:
- generated_from_trainer
datasets:
- lener_br
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-large-uncased-finetuned-ner-lenerBr
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: lener_br
      type: lener_br
      config: lener_br
      split: validation
      args: lener_br
    metrics:
    - name: Precision
      type: precision
      value: 0.8195459032576505
    - name: Recall
      type: recall
      value: 0.8534128289473685
    - name: F1
      type: f1
      value: 0.8361365696444758
    - name: Accuracy
      type: accuracy
      value: 0.9658050781203017
---

<!-- 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. -->

# bert-large-uncased-finetuned-ner-lenerBr

This model is a fine-tuned version of [google-bert/bert-large-uncased](https://huggingface.co/google-bert/bert-large-uncased) on the lener_br dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Precision: 0.8195
- Recall: 0.8534
- F1: 0.8361
- Accuracy: 0.9658

## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.9995 | 489  | nan             | 0.6811    | 0.7451 | 0.7116 | 0.9503   |
| 0.1982        | 1.9990 | 978  | nan             | 0.7258    | 0.8314 | 0.7750 | 0.9536   |
| 0.0517        | 2.9985 | 1467 | nan             | 0.7487    | 0.8238 | 0.7845 | 0.9587   |
| 0.0289        | 4.0    | 1957 | nan             | 0.7801    | 0.8684 | 0.8219 | 0.9641   |
| 0.0191        | 4.9995 | 2446 | nan             | 0.7986    | 0.8567 | 0.8266 | 0.9665   |
| 0.0138        | 5.9990 | 2935 | nan             | 0.8120    | 0.8491 | 0.8302 | 0.9642   |
| 0.0097        | 6.9985 | 3424 | nan             | 0.8201    | 0.8643 | 0.8416 | 0.9663   |
| 0.0076        | 8.0    | 3914 | nan             | 0.8079    | 0.8672 | 0.8365 | 0.9660   |
| 0.0053        | 8.9995 | 4403 | nan             | 0.8211    | 0.8409 | 0.8309 | 0.9662   |
| 0.0041        | 9.9949 | 4890 | nan             | 0.8195    | 0.8534 | 0.8361 | 0.9658   |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3