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metadata
library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
base_model: firqaaa/indo-sentence-bert-base
metrics:
  - accuracy
  - precision
  - recall
  - f1
widget:
  - text: >-
      halaman 97 - 128 tidak ada , diulang halaman 65 - 96 , pembelian hari
      minggu tanggal 24 desember sore sekitar jam 4 pembayaran menggunakan kartu
      atm bri bersamaan dengan buku the puppeteer dan sirkus pohon
  - text: liverpool sukses di kandang tottenham
  - text: >-
      hai angga , untuk penerbitan tiket reschedule diharuskan melakukan
      pembayaran dulu ya .
  - text: sedih kalau umat diprovokasi supaya saling membenci .
  - text: >-
      berada di lokasi strategis jalan merdeka , berseberangan agak ke samping
      bandung indah plaza , tapat sebelah kanan jalan sebelum traffic light ,
      parkir mobil cukup luas . saus bumbu dan lain-lain disediakan cukup
      lengkap di lantai bawah . di lantai atas suasana agak sepi . bakso cukup
      enak dan terjangkau harga nya tetapi kuah relatif kurang dan porsi tidak
      terlalu besar
pipeline_tag: text-classification
inference: true
model-index:
  - name: SetFit with firqaaa/indo-sentence-bert-base
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 0.7676767676767676
            name: Accuracy
          - type: precision
            value: 0.7676767676767676
            name: Precision
          - type: recall
            value: 0.7676767676767676
            name: Recall
          - type: f1
            value: 0.7676767676767676
            name: F1

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:

  1. Fine-tuning a Sentence Transformer with contrastive learning.
  2. 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 Sources

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}
}