autoevaluator
HF staff
Add evaluation results on the lener_br config and validation split of lener_br
787c714
language: | |
- pt | |
license: mit | |
tags: | |
- generated_from_trainer | |
datasets: | |
- lener_br | |
metrics: | |
- precision | |
- recall | |
- f1 | |
- accuracy | |
model-index: | |
- name: xlm-roberta-base-finetuned-lener_br-finetuned-lener-br | |
results: | |
- task: | |
name: Token Classification | |
type: token-classification | |
dataset: | |
name: lener_br | |
type: lener_br | |
config: lener_br | |
split: train | |
args: lener_br | |
metrics: | |
- name: Precision | |
type: precision | |
value: 0.9206349206349206 | |
- name: Recall | |
type: recall | |
value: 0.9294391315585423 | |
- name: F1 | |
type: f1 | |
value: 0.925016077170418 | |
- name: Accuracy | |
type: accuracy | |
value: 0.9832504071600401 | |
- 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: 0.9832802904657313 | |
verified: true | |
- name: Precision | |
type: precision | |
value: 0.986258771429967 | |
verified: true | |
- name: Recall | |
type: recall | |
value: 0.9897717432152019 | |
verified: true | |
- name: F1 | |
type: f1 | |
value: 0.9880121346555324 | |
verified: true | |
- name: loss | |
type: loss | |
value: 0.1050868034362793 | |
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. --> | |
# xlm-roberta-base-finetuned-lener_br-finetuned-lener-br | |
This model is a fine-tuned version of [Luciano/xlm-roberta-base-finetuned-lener_br](https://huggingface.co/Luciano/xlm-roberta-base-finetuned-lener_br) on the lener_br dataset. | |
It achieves the following results on the evaluation set: | |
- Loss: nan | |
- Precision: 0.9206 | |
- Recall: 0.9294 | |
- F1: 0.9250 | |
- Accuracy: 0.9833 | |
## 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 | |
- mixed_precision_training: Native AMP | |
### Training results | |
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | | |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | |
| 0.0657 | 1.0 | 1957 | nan | 0.7780 | 0.8687 | 0.8209 | 0.9718 | | |
| 0.0321 | 2.0 | 3914 | nan | 0.8755 | 0.8708 | 0.8731 | 0.9793 | | |
| 0.0274 | 3.0 | 5871 | nan | 0.8096 | 0.9124 | 0.8579 | 0.9735 | | |
| 0.0216 | 4.0 | 7828 | nan | 0.7913 | 0.8842 | 0.8352 | 0.9718 | | |
| 0.0175 | 5.0 | 9785 | nan | 0.7735 | 0.9248 | 0.8424 | 0.9721 | | |
| 0.0117 | 6.0 | 11742 | nan | 0.9206 | 0.9294 | 0.9250 | 0.9833 | | |
| 0.0121 | 7.0 | 13699 | nan | 0.8988 | 0.9318 | 0.9150 | 0.9819 | | |
| 0.0086 | 8.0 | 15656 | nan | 0.8922 | 0.9175 | 0.9047 | 0.9801 | | |
| 0.007 | 9.0 | 17613 | nan | 0.8482 | 0.8997 | 0.8732 | 0.9769 | | |
| 0.0051 | 10.0 | 19570 | nan | 0.8730 | 0.9274 | 0.8994 | 0.9798 | | |
| 0.0045 | 11.0 | 21527 | nan | 0.9172 | 0.9051 | 0.9111 | 0.9819 | | |
| 0.0014 | 12.0 | 23484 | nan | 0.9138 | 0.9155 | 0.9147 | 0.9823 | | |
| 0.0029 | 13.0 | 25441 | nan | 0.9099 | 0.9287 | 0.9192 | 0.9834 | | |
| 0.0035 | 14.0 | 27398 | nan | 0.9019 | 0.9294 | 0.9155 | 0.9831 | | |
| 0.0005 | 15.0 | 29355 | nan | 0.8886 | 0.9343 | 0.9109 | 0.9825 | | |
### Framework versions | |
- Transformers 4.23.1 | |
- Pytorch 1.12.1+cu113 | |
- Datasets 2.6.1 | |
- Tokenizers 0.13.1 | |