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:
- 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 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
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
model = SetFitModel.from_pretrained("ehsanhallo/setfit-paraphrase-multilingual-MiniLM-L12-v2-ig-fa")
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}
}