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---
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](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) 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](https://www.sbert.net) with contrastive learning.
2. 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](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 128 tokens
- **Number of Classes:** 3 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | <ul><li>'چه سودایی که سر همینا از دست دادم😂'</li><li>'خو فارسی بنویس بفهمه 😂😂😂😂😂'</li><li>'اینجا ایران همین سایتا هم\u200cزیادی..نیازی به بررسی ندارن...کلا دوسداریم به همچی ایراد بگیریم.'</li></ul> |
| 1 | <ul><li>'کد کارت مشکی NHKDKI'</li><li>'اتفاقا مسیولیت بیشتری برات میاره و درگیریات بیشتر میشه برای هدفی که داری'</li><li>'من میخام شروع کنم،اورج بفروشم یا فیک؟فیک ارزونتره ولی فیکه.اورجینال هم ک گرون تره ؟بنظرت اورج میخرن؟؟'</li></ul> |
| 2 | <ul><li>'🔥🔥🔥🔥'</li><li>'😂😂😂'</li><li>'چه قدر عالی وخفن 🔥🔥'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.79 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
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("ehsanhallo/setfit-paraphrase-multilingual-MiniLM-L12-v2-ig-fa")
# Run inference
preds = model(" where`d you go!")
```
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## 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
```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}
}
```
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