metadata
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: 0.761528608027327
- name: Recall
type: recall
value: 0.7616912235746316
- name: F1
type: f1
value: 0.7616099071207431
- name: Accuracy
type: accuracy
value: 0.9554657562878841
distilbert-base-multilingual-cased-finetuned-ner-lenerBr
This model is a fine-tuned version of distilbert/distilbert-base-multilingual-cased on the lener_br dataset. It achieves the following results on the evaluation set:
- Loss: 0.1792
- Precision: 0.7615
- Recall: 0.7617
- F1: 0.7616
- Accuracy: 0.9555
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 | 0.2100 | 0.7139 | 0.6624 | 0.6872 | 0.9394 |
| 0.2608 | 2.0 | 980 | 0.1962 | 0.7059 | 0.7508 | 0.7276 | 0.9443 |
| 0.0681 | 3.0 | 1470 | 0.1858 | 0.7225 | 0.7649 | 0.7431 | 0.9486 |
| 0.0382 | 4.0 | 1960 | 0.1792 | 0.7615 | 0.7617 | 0.7616 | 0.9555 |
| 0.0248 | 5.0 | 2450 | 0.2068 | 0.7715 | 0.8149 | 0.7926 | 0.9560 |
| 0.0173 | 6.0 | 2940 | 0.2029 | 0.7112 | 0.8031 | 0.7544 | 0.9529 |
Framework versions
- Transformers 4.41.1
- Pytorch 2.1.2
- Datasets 2.19.1
- Tokenizers 0.19.1