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Add SetFit model
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metadata
library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
widget:
  - text: ' i still dont know what we would do though'
  - text: ' where`d you go!'
  - text: ' Thank you!  I`m working on `s'
  - text: Terminator Salvation... by myself.
  - text: ' lol man i got 2 1 /2 hrs an iont how i woulda made it wit out my ramen noodles and t.v. Time'
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
model-index:
  - name: SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 0.79
            name: Accuracy

SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

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:

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

Model Details

Model Description

Model Sources

Model Labels

Label Examples
0
  • 'چه سودایی که سر همینا از دست دادم😂'
  • 'خو فارسی بنویس بفهمه 😂😂😂😂😂'
  • 'اینجا ایران همین سایتا هم\u200cزیادی..نیازی به بررسی ندارن...کلا دوسداریم به همچی ایراد بگیریم.'
1
  • 'کد کارت مشکی NHKDKI'
  • 'اتفاقا مسیولیت بیشتری برات میاره و درگیریات بیشتر میشه برای هدفی که داری'
  • 'من میخام شروع کنم،اورج بفروشم یا فیک؟فیک ارزونتره ولی فیکه.اورجینال هم ک گرون تره ؟بنظرت اورج میخرن؟؟'
2
  • '🔥🔥🔥🔥'
  • '😂😂😂'
  • 'چه قدر عالی وخفن 🔥🔥'

Evaluation

Metrics

Label Accuracy
all 0.79

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("ehsanhallo/setfit-paraphrase-multilingual-MiniLM-L12-v2-ig-fa")
# Run inference
preds = model(" where`d you go!")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 1 6.4184 75
Label Training Sample Count
0 69
1 238
2 551

Training Hyperparameters

  • batch_size: (32, 16)
  • num_epochs: (1, 2)
  • max_steps: -1
  • sampling_strategy: oversampling
  • body_learning_rate: (2e-05, 5e-06)
  • head_learning_rate: 0.002
  • 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: True

Training Results

Epoch Step Training Loss Validation Loss
0.0001 1 0.1767 -
0.0216 250 0.1513 -
0.0431 500 0.0629 0.2389
0.0647 750 0.0351 -
0.0862 1000 0.0015 0.1886
0.1078 1250 0.0003 -
0.1293 1500 0.0004 0.1813
0.1509 1750 0.0002 -
0.1724 2000 0.0002 0.1807
0.1940 2250 0.0001 -
0.2155 2500 0.0001 0.187
0.2371 2750 0.0001 -
0.2586 3000 0.0001 0.1903
0.2802 3250 0.0001 -
0.3018 3500 0.0 0.1864
0.3233 3750 0.0 -
0.3449 4000 0.0 0.193
0.3664 4250 0.0 -
0.3880 4500 0.0 0.1879
0.4095 4750 0.0 -
0.4311 5000 0.0 0.1887
0.4526 5250 0.0 -
0.4742 5500 0.0 0.187
0.4957 5750 0.0 -
0.5173 6000 0.0001 0.205
0.5388 6250 0.0 -
0.5604 6500 0.0 0.205
0.5819 6750 0.0 -
0.6035 7000 0.0 0.2018
0.6251 7250 0.0 -
0.6466 7500 0.0 0.2022
0.6682 7750 0.0 -
0.6897 8000 0.0 0.2063
0.7113 8250 0.0 -
0.7328 8500 0.0 0.2143
0.7544 8750 0.0 -
0.7759 9000 0.0 0.2206
0.7975 9250 0.0 -
0.8190 9500 0.0 0.2167
0.8406 9750 0.0 -
0.8621 10000 0.0 0.2176
0.8837 10250 0.0 -
0.9053 10500 0.0 0.217
0.9268 10750 0.0 -
0.9484 11000 0.0 0.2153
0.9699 11250 0.0 -
0.9915 11500 0.0 0.2137
  • The bold row denotes the saved checkpoint.

Framework Versions

  • Python: 3.10.12
  • SetFit: 1.0.1
  • Sentence Transformers: 2.2.2
  • Transformers: 4.35.2
  • PyTorch: 2.1.0+cu121
  • Datasets: 2.16.1
  • Tokenizers: 0.15.0

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}
}