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---
license: apache-2.0
base_model: distilbert/distilroberta-base
tags:
- generated_from_trainer
datasets:
- lener_br
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilroberta-base-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: 0.801254136909946
- name: Recall
type: recall
value: 0.8429540040315191
- name: F1
type: f1
value: 0.821575281300232
- name: Accuracy
type: accuracy
value: 0.9685663231476382
---
<!-- 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. -->
# distilroberta-base-finetuned-ner-lenerBr
This model is a fine-tuned version of [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on the lener_br dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1550
- Precision: 0.8013
- Recall: 0.8430
- F1: 0.8216
- Accuracy: 0.9686
## 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 | 0.1750 | 0.7347 | 0.6581 | 0.6942 | 0.9465 |
| 0.2808 | 2.0 | 980 | 0.1642 | 0.6954 | 0.7598 | 0.7262 | 0.9538 |
| 0.093 | 3.0 | 1470 | 0.1849 | 0.6708 | 0.7992 | 0.7294 | 0.9510 |
| 0.0557 | 4.0 | 1960 | 0.1403 | 0.7807 | 0.8345 | 0.8067 | 0.9668 |
| 0.0366 | 5.0 | 2450 | 0.1560 | 0.7775 | 0.8466 | 0.8106 | 0.9626 |
| 0.027 | 6.0 | 2940 | 0.1612 | 0.7342 | 0.8239 | 0.7764 | 0.9621 |
| 0.0204 | 7.0 | 3430 | 0.1632 | 0.7625 | 0.8356 | 0.7974 | 0.9644 |
| 0.015 | 8.0 | 3920 | 0.1748 | 0.7375 | 0.8442 | 0.7873 | 0.9615 |
| 0.0135 | 9.0 | 4410 | 0.1547 | 0.7930 | 0.8446 | 0.8180 | 0.9685 |
| 0.0101 | 10.0 | 4900 | 0.1550 | 0.8013 | 0.8430 | 0.8216 | 0.9686 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.1.2
- Datasets 2.19.1
- Tokenizers 0.19.1
|