File size: 3,720 Bytes
71de300 db6c675 71de300 958890c 71de300 958890c 71de300 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 |
---
license: mit
tags:
- generated_from_trainer
datasets:
- indonlu
metrics:
- accuracy
- f1
model-index:
- name: Fine-tuned-Indonesian-Sentiment-Classifier
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: 0.9317460317460318
- name: F1
type: f1
value: 0.9034223843742829
language:
- id
pipeline_tag: text-classification
widget:
- text: "Kalo kamu WFH emang kerja?"
- text: "buku ini kurang bagus isinya"
---
<!-- 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. -->
# Fine-tuned-Indonesian-Sentiment-Classifier
This model is a fine-tuned version of [indobenchmark/indobert-base-p1](https://huggingface.co./indobenchmark/indobert-base-p1) on the [IndoNLU's SmSA](https://huggingface.co./datasets/indonlp/indonlu) dataset.
It achieves the following results on the evaluation dataset:
- Loss: 0.3233
- Accuracy: 0.9317
- F1: 0.9034
And the results of the test dataset:
- Accuracy: 0.928
- F1 macro: 0.9113470780757361
- F1 micro: 0.928
- F1 weighted: 0.9261959965604815
## Model description
This model can be used to determine the sentiment of a text with three possible outputs [positive, negative, or neutral]
## How to use
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
Pre-trained = "hanifnoerr/Fine-tuned-Indonesian-Sentiment-Classifier"
tokenizer = AutoTokenizer.from_pretrained(Pre-trained)
model = AutoModelForSequenceClassification.from_pretrained(Pre-trained)
```
### make classification
```python
pretrained_name = "hanifnoerr/Fine-tuned-Indonesian-Sentiment-Classifier"
sentimen = pipeline(tokenizer=pretrained_name, model=pretrained_name)
kalimat = "buku ini jelek sekali"
sentimen(kalimat)
```
output: [{'label': 'negative', 'score': 0.9996247291564941}]
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.08 | 1.0 | 688 | 0.3532 | 0.9310 | 0.9053 |
| 0.0523 | 2.0 | 1376 | 0.3233 | 0.9317 | 0.9034 |
| 0.045 | 3.0 | 2064 | 0.3949 | 0.9286 | 0.8995 |
| 0.0252 | 4.0 | 2752 | 0.4662 | 0.9310 | 0.9049 |
| 0.0149 | 5.0 | 3440 | 0.6251 | 0.9246 | 0.8899 |
| 0.0091 | 6.0 | 4128 | 0.6148 | 0.9254 | 0.8928 |
| 0.0111 | 7.0 | 4816 | 0.6259 | 0.9222 | 0.8902 |
| 0.0106 | 8.0 | 5504 | 0.6123 | 0.9238 | 0.8882 |
| 0.0092 | 9.0 | 6192 | 0.6353 | 0.9230 | 0.8928 |
| 0.0085 | 10.0 | 6880 | 0.6733 | 0.9254 | 0.8989 |
| 0.0062 | 11.0 | 7568 | 0.6666 | 0.9302 | 0.9027 |
| 0.0036 | 12.0 | 8256 | 0.7578 | 0.9230 | 0.8962 |
| 0.0055 | 13.0 | 8944 | 0.7378 | 0.9270 | 0.8947 |
| 0.0023 | 14.0 | 9632 | 0.7758 | 0.9230 | 0.8978 |
| 0.0009 | 15.0 | 10320 | 0.7051 | 0.9278 | 0.9006 |
| 0.0033 | 16.0 | 11008 | 0.7442 | 0.9214 | 0.8902 |
| 0.0 | 17.0 | 11696 | 0.7513 | 0.9254 | 0.8974 |
| 0.0 | 18.0 | 12384 | 0.7554 | 0.9270 | 0.8999 |
Although trained with 18 epochs, this model uses the best weight (Epoch 2)
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3 |