Sentiment Analysis for TWS Reviews
This model is a fine-tuned version of w11wo/indonesian-roberta-base-sentiment-classifier on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2671
- Accuracy: 0.9051
- Precision: 0.9004
- Recall: 0.9051
- F1: 0.9007
How to Use
from transformers import pipeline
# Buat pipeline klasifikasi menggunakan model dari Hugging Face Hub
model_name = "ragilbuaj/sentiment-analysis-TWS-reviews"
classifier = pipeline("sentiment-analysis", model=model_name, tokenizer=model_name)
# Gunakan pipeline untuk mengklasifikasikan teks
result = classifier("I love using Hugging Face models!")
print(result)
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.2786 | 0.9992 | 589 | 0.2370 | 0.9109 | 0.9053 | 0.9109 | 0.9060 |
| 0.2205 | 2.0 | 1179 | 0.2626 | 0.9143 | 0.9175 | 0.9143 | 0.9154 |
| 0.1252 | 2.9992 | 1768 | 0.3468 | 0.9169 | 0.9160 | 0.9169 | 0.9164 |
| 0.0869 | 4.0 | 2358 | 0.4000 | 0.9220 | 0.9200 | 0.9220 | 0.9208 |
| 0.0147 | 4.9958 | 2945 | 0.4424 | 0.9220 | 0.9180 | 0.9220 | 0.9194 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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