This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.

The model has been trained using an efficient few-shot learning technique that involves:

Fine-tuning a Sentence Transformer with contrastive learning.
Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

Model Type: SetFit
Sentence Transformer body: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
Classification head: a LogisticRegression instance
Maximum Sequence Length: 128 tokens
Number of Classes: 3 classes

Model Sources

Repository: SetFit on GitHub
Paper: Efficient Few-Shot Learning Without Prompts
Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

Model Labels

Label	Examples
2	'Saya merasa tidak aman saat naik kendaraan umum malam hari.' 'Banyak kendaraan parkir sembarangan di sekitar sini.' 'ini bukan indonesia yang lubang jalannya kayak kawah gunung bromo'
1	'Keberhasilan belajar murid tidak hanya bergantung pada jumlah dan mutu guru yang memadai' 'kampungan sekali aku' 'Kendaraan baru ini sangat nyaman untuk dikendarai.'
0	'Tabel 5.2 memperlihatkan hubungan antara hasil tes murid dengan tingkat keabsenan guru' 'Kalimat-kalimat tersebut adalah sebagai berikut' 'Tanaman hijau menggunakan air untuk membuat makanannya'

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

python

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("Yakobbabis/sarkasmeter-indonesia-v1")
# Run inference
preds = model("bisa terkena tamparan anda ni")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	3	9.4545	18

Label	Training Sample Count
0	4
1	7
2	22

Training Hyperparameters

batch_size: (16, 16)
num_epochs: (3, 3)
max_steps: -1
sampling_strategy: oversampling
num_iterations: 20
body_learning_rate: (2e-05, 2e-05)
head_learning_rate: 2e-05
loss: CosineSimilarityLoss
distance_metric: cosine_distance
margin: 0.25
end_to_end: False
use_amp: False
warmup_proportion: 0.1
l2_weight: 0.01
seed: 42
eval_max_steps: -1
load_best_model_at_end: False

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0120	1	0.3674	-
0.6024	50	0.1704	-
1.2048	100	0.0193	-
1.8072	150	0.005	-
2.4096	200	0.0022	-

Framework Versions

Python: 3.12.12
SetFit: 1.1.3
Sentence Transformers: 5.1.2
Transformers: 4.57.2
PyTorch: 2.9.0+cu126
Datasets: 4.0.0
Tokenizers: 0.22.1

Citation

BibTeX

bibtex

@article{https://doi.org/10.48550/arxiv.2209.11055,
    doi = {10.48550/ARXIV.2209.11055},
    url = {https://arxiv.org/abs/2209.11055},
    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
    title = {Efficient Few-Shot Learning Without Prompts},
    publisher = {arXiv},
    year = {2022},
    copyright = {Creative Commons Attribution 4.0 International}
}