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autoevaluator
HF staff
Add evaluation results on the lener_br config and train split of lener_br
14d5a4d
language: | |
- pt | |
license: mit | |
tags: | |
- generated_from_trainer | |
datasets: | |
- lener_br | |
metrics: | |
- precision | |
- recall | |
- f1 | |
- accuracy | |
model_index: | |
- name: bertimbau-large-lener_br | |
results: | |
- task: | |
name: Token Classification | |
type: token-classification | |
dataset: | |
name: lener_br | |
type: lener_br | |
args: lener_br | |
metric: | |
name: Accuracy | |
type: accuracy | |
value: 0.9801301293674859 | |
model-index: | |
- name: Luciano/bertimbau-large-lener_br | |
results: | |
- task: | |
type: token-classification | |
name: Token Classification | |
dataset: | |
name: lener_br | |
type: lener_br | |
config: lener_br | |
split: test | |
metrics: | |
- name: Accuracy | |
type: accuracy | |
value: 0.9840898731012984 | |
verified: true | |
- name: Precision | |
type: precision | |
value: 0.9895415357344292 | |
verified: true | |
- name: Recall | |
type: recall | |
value: 0.9885856878370763 | |
verified: true | |
- name: F1 | |
type: f1 | |
value: 0.9890633808488363 | |
verified: true | |
- name: loss | |
type: loss | |
value: 0.10151929408311844 | |
verified: true | |
- task: | |
type: token-classification | |
name: Token Classification | |
dataset: | |
name: lener_br | |
type: lener_br | |
config: lener_br | |
split: validation | |
metrics: | |
- name: Accuracy | |
type: accuracy | |
value: 0.9801301293674859 | |
verified: true | |
- name: Precision | |
type: precision | |
value: 0.9864285473144053 | |
verified: true | |
- name: Recall | |
type: recall | |
value: 0.9845505854603656 | |
verified: true | |
- name: F1 | |
type: f1 | |
value: 0.9854886717201953 | |
verified: true | |
- name: loss | |
type: loss | |
value: 0.11984097212553024 | |
verified: true | |
- task: | |
type: token-classification | |
name: Token Classification | |
dataset: | |
name: lener_br | |
type: lener_br | |
config: lener_br | |
split: train | |
metrics: | |
- name: Accuracy | |
type: accuracy | |
value: 0.9989004979420315 | |
verified: true | |
- name: Precision | |
type: precision | |
value: 0.9991129612205654 | |
verified: true | |
- name: Recall | |
type: recall | |
value: 0.9993219071519783 | |
verified: true | |
- name: F1 | |
type: f1 | |
value: 0.9992174232631231 | |
verified: true | |
- name: loss | |
type: loss | |
value: 0.0037613145541399717 | |
verified: true | |
<!-- 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. --> | |
# bertimbau-large-lener_br | |
This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on the lener_br dataset. | |
It achieves the following results on the evaluation set: | |
- Loss: 0.1271 | |
- Precision: 0.8965 | |
- Recall: 0.9198 | |
- F1: 0.9080 | |
- Accuracy: 0.9801 | |
## 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: 4 | |
- eval_batch_size: 4 | |
- seed: 42 | |
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
- lr_scheduler_type: linear | |
- num_epochs: 15 | |
### Training results | |
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | | |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | |
| 0.0674 | 1.0 | 1957 | 0.1349 | 0.7617 | 0.8710 | 0.8127 | 0.9594 | | |
| 0.0443 | 2.0 | 3914 | 0.1867 | 0.6862 | 0.9194 | 0.7858 | 0.9575 | | |
| 0.0283 | 3.0 | 5871 | 0.1185 | 0.8206 | 0.8766 | 0.8477 | 0.9678 | | |
| 0.0226 | 4.0 | 7828 | 0.1405 | 0.8072 | 0.8978 | 0.8501 | 0.9708 | | |
| 0.0141 | 5.0 | 9785 | 0.1898 | 0.7224 | 0.9194 | 0.8090 | 0.9629 | | |
| 0.01 | 6.0 | 11742 | 0.1655 | 0.9062 | 0.8856 | 0.8958 | 0.9741 | | |
| 0.012 | 7.0 | 13699 | 0.1271 | 0.8965 | 0.9198 | 0.9080 | 0.9801 | | |
| 0.0091 | 8.0 | 15656 | 0.1919 | 0.8890 | 0.8886 | 0.8888 | 0.9719 | | |
| 0.0042 | 9.0 | 17613 | 0.1725 | 0.8977 | 0.8985 | 0.8981 | 0.9744 | | |
| 0.0043 | 10.0 | 19570 | 0.1530 | 0.8878 | 0.9034 | 0.8955 | 0.9761 | | |
| 0.0042 | 11.0 | 21527 | 0.1635 | 0.8792 | 0.9108 | 0.8947 | 0.9774 | | |
| 0.0033 | 12.0 | 23484 | 0.2009 | 0.8155 | 0.9138 | 0.8619 | 0.9719 | | |
| 0.0008 | 13.0 | 25441 | 0.1766 | 0.8737 | 0.9135 | 0.8932 | 0.9755 | | |
| 0.0005 | 14.0 | 27398 | 0.1868 | 0.8616 | 0.9129 | 0.8865 | 0.9743 | | |
| 0.0014 | 15.0 | 29355 | 0.1910 | 0.8694 | 0.9101 | 0.8893 | 0.9746 | | |
### Framework versions | |
- Transformers 4.8.2 | |
- Pytorch 1.9.0+cu102 | |
- Datasets 1.9.0 | |
- Tokenizers 0.10.3 | |