---
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
datasets:
- id_nergrit_corpus
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: indobert-model-ner
  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: 0.8173884586015054
    - name: Recall
      type: recall
      value: 0.8428571428571429
    - name: F1
      type: f1
      value: 0.8299274519576895
    - name: Accuracy
      type: accuracy
      value: 0.9513388777896514
---

<!-- 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. -->

# indobert-model-ner

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: 0.2175
- Precision: 0.8174
- Recall: 0.8429
- F1: 0.8299
- Accuracy: 0.9513

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0534        | 1.0   | 784  | 0.2316          | 0.7964    | 0.8286 | 0.8122 | 0.9485   |
| 0.0715        | 2.0   | 1568 | 0.2046          | 0.8186    | 0.8321 | 0.8253 | 0.9517   |
| 0.0661        | 3.0   | 2352 | 0.2175          | 0.8174    | 0.8429 | 0.8299 | 0.9513   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3