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---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- lener_br
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-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: 0.7477750426055672
- name: Recall
type: recall
value: 0.8118832236842105
- name: F1
type: f1
value: 0.7785115820601283
- name: Accuracy
type: accuracy
value: 0.9644699967525048
---
<!-- 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. -->
# distilbert-base-uncased-finetuned-ner-lenerBr
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the lener_br dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1546
- Precision: 0.7478
- Recall: 0.8119
- F1: 0.7785
- Accuracy: 0.9645
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 490 | 0.2131 | 0.6201 | 0.6604 | 0.6396 | 0.9359 |
| 0.264 | 2.0 | 980 | 0.1828 | 0.7004 | 0.7504 | 0.7246 | 0.9508 |
| 0.0776 | 3.0 | 1470 | 0.1564 | 0.6582 | 0.8137 | 0.7278 | 0.9537 |
| 0.0437 | 4.0 | 1960 | 0.1644 | 0.7485 | 0.7623 | 0.7553 | 0.9573 |
| 0.0288 | 5.0 | 2450 | 0.1555 | 0.7620 | 0.7662 | 0.7641 | 0.9614 |
| 0.0208 | 6.0 | 2940 | 0.1874 | 0.7530 | 0.7759 | 0.7643 | 0.9550 |
| 0.0143 | 7.0 | 3430 | 0.1546 | 0.7478 | 0.8119 | 0.7785 | 0.9645 |
| 0.0117 | 8.0 | 3920 | 0.1717 | 0.7014 | 0.7677 | 0.7330 | 0.9592 |
| 0.0102 | 9.0 | 4410 | 0.1884 | 0.7734 | 0.7714 | 0.7724 | 0.9613 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.1.2
- Datasets 2.19.1
- Tokenizers 0.19.1
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