---
library_name: transformers
license: mit
base_model: indobenchmark/indobert-base-p1
tags:
- generated_from_trainer
datasets:
- indonlu
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: Model_analisis_sentimen
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: indonlu
      type: indonlu
      config: smsa
      split: validation
      args: smsa
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9412698412698413
    - name: Precision
      type: precision
      value: 0.9167407809931684
    - name: Recall
      type: recall
      value: 0.9068353459620502
    - name: F1
      type: f1
      value: 0.9115530488925131
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Model_analisis_sentimen

This model is a fine-tuned version of [indobenchmark/indobert-base-p1](https://huggingface.co/indobenchmark/indobert-base-p1) on the indonlu dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4175
- Accuracy: 0.9413
- Precision: 0.9167
- Recall: 0.9068
- F1: 0.9116

## 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: 10

### Training results



### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1