---
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!")
```

<!--
### Downstream Use

*List how someone could finetune this model on their own dataset.*
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## 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}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->