SetFit with firqaaa/indo-sentence-bert-base for indonlu/smsa

Author

Kelompok 3 :

Muhammad Guntur Arfianto (20/459272/PA/19933)
Putri Iqlima Miftahuddini (23/531392/NUGM/01467)
Alan Kurniawan (23/531301/NUGM/01382)

This is a SetFit model that can be used for Text Classification. This SetFit model uses firqaaa/indo-sentence-bert-base 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.

The dataset that was used for fine-tuning this model is indonlu, specifically its subset, SmSa dataset.

Model Details

Model Description

Model Type: SetFit
Sentence Transformer body: firqaaa/indo-sentence-bert-base
Classification head: a LogisticRegression instance
Maximum Sequence Length: 512 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	'hampir semua musala di stasiun jalur ke bogor kondisi nya juga terlalu sempit dan fasilitas wudhu yang kurang . bahkan sekelas stasiun besar bogor .' 'tangkap saja pak si penyanyi gadungan itu . kerjaan nya cuma fitnah di media sosial saja .' 'saya di cgv marvel city sby mau verifikasi sms redam , tapi di informasi telkomsel trobel , menyebalkan !'
1	'bapak berkumis lebat itu menyebrang menggunakan zebra cross' 'kaitan kalung cantik bahan perak / silver 925' 'duo red bull mendominasi latihan bebas pertama f1 gp singapura'
0	'jokowi sayang dan cinta kepada rakyat nya' 'nyaman banget kalau lagi nongkrong kenyang di warung upnormal . mulai dari pilihan menu nya yang serius banget digarap , dari pelayan2 nya yang kece , sampai ke interior nya yang super . rekomendasi banget deh kalau mau mengerjakan tugas , arisan , ulang tahun , reunian di sini .' 'rasanya lumayan . sambel nya juga enak . apalagi disajikan 3 macam model begitu . terus banyak pilihan sih sebenarnya mau makan apa di sini . mau gurame , mau kakap , bawal , kerang , cumi , udang . macem-macem deh . asal jangan pesan ikan kembung saja . tidak ada di sini .'

Evaluation

Metrics

Label	Accuracy	Precision	Recall	F1
all	0.7677	0.7677	0.7677	0.7677

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("TRUEnder/setfit-indosentencebert-indonlusmsa-16-shot")
# Run inference
preds = model("liverpool sukses di kandang tottenham")

Training Details

Training Set Metrics

Label	Training Sample Count
0	16
1	16
2	16

Training Hyperparameters

batch_size: (16, 2)
num_epochs: (6, 16)
max_steps: -1
sampling_strategy: oversampling
body_learning_rate: (2e-05, 1e-05)
head_learning_rate: 0.01
loss: CosineSimilarityLoss
distance_metric: cosine_distance
margin: 0.25
end_to_end: False
use_amp: False
warmup_proportion: 0.1
seed: 42
eval_max_steps: -1
load_best_model_at_end: True

Training Results (Epoch-to-epoch)

Epoch	Step	Training Loss	Validation Loss
1.0	96	0.0009	0.1923
2.0	192	0.0002	0.1977
3.0	288	0.0002	0.2011
4.0	384	0.0002	0.203
5.0	480	0.0001	0.2042
6.0	576	0.0001	0.2046

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
SetFit: 1.0.3
Sentence Transformers: 3.0.1
Transformers: 4.41.2
PyTorch: 2.3.0+cu121
Datasets: 2.19.2
Tokenizers: 0.19.1

Citation

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}
}

TRUEnder
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setfit-indosentencebert-indonlusmsa-16-shot