transformers safetensors bert text-classification generated_from_trainer base_model:indobenchmark/indobert-large-p1 license:mit autotrain_compatible endpoints_compatible region:us
---
license: mit
base_model: indobenchmark/indobert-large-p1
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: indobert-large-p1-reddit-indonesia-sarcastic
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# indobert-large-p1-reddit-indonesia-sarcastic

This model is a fine-tuned version of [indobenchmark/indobert-large-p1](https://huggingface.co/indobenchmark/indobert-large-p1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4486
- Accuracy: 0.7911
- F1: 0.6184
- Precision: 0.5690
- Recall: 0.6771

## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 100.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.4573        | 1.0   | 309  | 0.4251          | 0.7966   | 0.5684 | 0.6058    | 0.5354 |
| 0.3274        | 2.0   | 618  | 0.4458          | 0.7824   | 0.5955 | 0.5567    | 0.6402 |
| 0.1999        | 3.0   | 927  | 0.5890          | 0.8065   | 0.5412 | 0.6653    | 0.4561 |
| 0.0864        | 4.0   | 1236 | 0.8080          | 0.8023   | 0.5536 | 0.6360    | 0.4901 |
| 0.0391        | 5.0   | 1545 | 1.1299          | 0.7895   | 0.5293 | 0.6007    | 0.4731 |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
