|
--- |
|
license: mit |
|
base_model: indolem/indobert-base-uncased |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- id_nergrit_corpus |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: KIPBERT |
|
results: |
|
- task: |
|
name: Token Classification |
|
type: token-classification |
|
dataset: |
|
name: id_nergrit_corpus |
|
type: id_nergrit_corpus |
|
config: ner |
|
split: test |
|
args: ner |
|
metrics: |
|
- name: Precision |
|
type: precision |
|
value: 0.8057702776265651 |
|
- name: Recall |
|
type: recall |
|
value: 0.8325084364454444 |
|
- name: F1 |
|
type: f1 |
|
value: 0.8189211618257262 |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.9503167154516874 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# KIPBERT |
|
|
|
This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on the id_nergrit_corpus dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.1731 |
|
- Precision: 0.8058 |
|
- Recall: 0.8325 |
|
- F1: 0.8189 |
|
- Accuracy: 0.9503 |
|
|
|
## 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: 2 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| 0.4926 | 1.0 | 784 | 0.1810 | 0.7860 | 0.8172 | 0.8013 | 0.9450 | |
|
| 0.1627 | 2.0 | 1568 | 0.1731 | 0.8058 | 0.8325 | 0.8189 | 0.9503 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.33.2 |
|
- Pytorch 2.0.1+cu118 |
|
- Datasets 2.14.5 |
|
- Tokenizers 0.13.3 |
|
|
