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---
license: apache-2.0
base_model: distilbert/distilbert-base-multilingual-cased
tags:
- generated_from_trainer
datasets:
- lener_br
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-multilingual-cased-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: 0.7959714100064977
- name: Recall
type: recall
value: 0.7847533632286996
- name: F1
type: f1
value: 0.7903225806451614
- name: Accuracy
type: accuracy
value: 0.959060823521215
---
<!-- 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-multilingual-cased-finetuned-ner-lenerBr
This model is a fine-tuned version of [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased) on the lener_br dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1904
- Precision: 0.7960
- Recall: 0.7848
- F1: 0.7903
- Accuracy: 0.9591
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 490 | 0.2124 | 0.6794 | 0.6842 | 0.6818 | 0.9363 |
| 0.2601 | 2.0 | 980 | 0.1744 | 0.701 | 0.7485 | 0.7239 | 0.9486 |
| 0.0688 | 3.0 | 1470 | 0.1653 | 0.7344 | 0.7598 | 0.7469 | 0.9522 |
| 0.0375 | 4.0 | 1960 | 0.1868 | 0.7764 | 0.7429 | 0.7593 | 0.9546 |
| 0.0229 | 5.0 | 2450 | 0.1844 | 0.7748 | 0.7854 | 0.7801 | 0.9560 |
| 0.0162 | 6.0 | 2940 | 0.2072 | 0.6896 | 0.7929 | 0.7377 | 0.9462 |
| 0.0123 | 7.0 | 3430 | 0.1941 | 0.7612 | 0.7704 | 0.7658 | 0.9548 |
| 0.0078 | 8.0 | 3920 | 0.1900 | 0.7701 | 0.7909 | 0.7804 | 0.9581 |
| 0.0068 | 9.0 | 4410 | 0.1884 | 0.8000 | 0.7822 | 0.7910 | 0.9593 |
| 0.0045 | 10.0 | 4900 | 0.1904 | 0.7960 | 0.7848 | 0.7903 | 0.9591 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.1.2
- Datasets 2.19.1
- Tokenizers 0.19.1
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