KIPBERT / README.md
kiipliwooke's picture
End of training
7334630
|
raw
history blame
2.16 kB
---
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