|
--- |
|
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 |
|
|
