---
license: apache-2.0
base_model: AptaArkana/indonesian-emotion-distilbert-base-cased-finetuned
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: DistilBERT-Rating-Prediction
  results: []
---

<!-- 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. -->

# DistilBERT-Rating-Prediction

This model is a fine-tuned version of [AptaArkana/indonesian-emotion-distilbert-base-cased-finetuned](https://huggingface.co/AptaArkana/indonesian-emotion-distilbert-base-cased-finetuned) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9357
- F1: 0.3927

## 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: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.15
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5927        | 1.0   | 91   | 0.8094          | 0.3878 |
| 0.4851        | 2.0   | 182  | 0.8268          | 0.3846 |
| 0.2956        | 3.0   | 273  | 0.8444          | 0.3809 |
| 0.1669        | 4.0   | 364  | 0.8558          | 0.3738 |
| 0.2344        | 5.0   | 455  | 0.8772          | 0.3688 |
| 0.0776        | 6.0   | 546  | 0.8992          | 0.3746 |
| 0.2512        | 7.0   | 637  | 0.9313          | 0.3781 |
| 0.1181        | 8.0   | 728  | 0.9292          | 0.3796 |
| 0.2482        | 9.0   | 819  | 0.9284          | 0.3889 |
| 0.6544        | 10.0  | 910  | 0.9357          | 0.3927 |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2