kornwtp/smsa-ind-classification
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How to use arndri/indobertweet-finetuned-indonlu-smsa with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="arndri/indobertweet-finetuned-indonlu-smsa") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("arndri/indobertweet-finetuned-indonlu-smsa")
model = AutoModelForSequenceClassification.from_pretrained("arndri/indobertweet-finetuned-indonlu-smsa", device_map="auto")| Epoch | Training Loss | Validation Loss |
|---|---|---|
| 1 | 0.149900 | 0.139475 |
| 2 | 0.131600 | 0.143117 |
| 3 | 0.036600 | 0.192144 |
| Class | Precision | Recall | F1-Score | Support |
|---|---|---|---|---|
| Positive | 0.98 | 0.94 | 0.96 | 1098 |
| Negative | 0.89 | 0.96 | 0.93 | 601 |
| Accuracy | 0.95 | 1699 | ||
| Macro Avg | 0.93 | 0.95 | 0.94 | 1699 |
| Weighted Avg | 0.95 | 0.95 | 0.95 | 1699 |
If you use this model, please cite:
A. Pratama and M. Rosyda, “ANALISIS SENTIMEN DALAM APLIKASI X TERHADAP PENGUNGSI ROHINGYA DENGAN LSTM”, SKANIKA, vol. 8, no. 1, pp. 95-105, Jan. 2025.
Base model
indolem/indobertweet-base-uncased