
# SetFit with sentence-transformers/all-mpnet-base-v2

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-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/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 384 tokens
- **Number of Classes:** 3 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
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### 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                                                                                                                                                                                                                                                                                                                                                                                                                          |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 2     | <ul><li>'validity airtel xstream fiber id 20001896982 expire 04-sep-23 . please recharge rs 589 enjoy uninterrupted service . recharge , click www.airtel.in/5/c_summary ? n=021710937343_dsl . please ignore already pay .'</li><li>'initiate process add a/c . xxxx59 upi app - axis bank'</li><li>'google-pay registration initiate icici bank . do , report bank . card details/otp/cvv secret . disclose anyone .'</li></ul> |
| 0     | <ul><li>'rs 260.00 debit a/c xxxxxx7783 credit krjngm @ oksbi upi ref:325154274303. ? call 18005700 -bob'</li><li>'send rs.400.00 kotak bank ac x4524 7800600122 @ ybl 15-10-23.upi ref 328855774953. , kotak.com/fraud'</li><li>'send rs.400.00 kotak bank ac x4524 7800600122 @ ybl 15-10-23.upi ref 328855774953. , kotak.com/fraud'</li></ul>                                                                                 |
| 1     | <ul><li>'dear bob upi user , account credit inr 50.00 date 2023-08-27 11:41:09 upi ref 360562629741 - bob'</li><li>'receive rs.10000.00 kotak bank ac x4524 mahimagyamlani08 @ okaxis 21-08-23.bal:197,838.14.upi ref:323334598750'</li><li>'update ! inr5.66 credit federal bank account xxxx9374 jupiter app . happy bank !'</li></ul>                                                                                          |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.9722   |

## 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("vipinbansal179/SetFit_sms_Analyzer5c95292")
# Run inference
preds = model("< # > use otp : 8233 login turtlemintpro zck+rfoaqnm")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 5   | 20.5357 | 35  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 31                    |
| 1     | 28                    |
| 2     | 81                    |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (4, 4)
- 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   | Step    | Training Loss | Validation Loss |
|:-------:|:-------:|:-------------:|:---------------:|
| 0.0014  | 1       | 0.2939        | -               |
| 0.0708  | 50      | 0.1698        | -               |
| 0.1416  | 100     | 0.0557        | -               |
| 0.2125  | 150     | 0.0614        | -               |
| 0.2833  | 200     | 0.0099        | -               |
| 0.3541  | 250     | 0.0005        | -               |
| 0.4249  | 300     | 0.0002        | -               |
| 0.4958  | 350     | 0.0001        | -               |
| 0.5666  | 400     | 0.0001        | -               |
| 0.6374  | 450     | 0.0001        | -               |
| 0.7082  | 500     | 0.0001        | -               |
| 0.7790  | 550     | 0.0001        | -               |
| 0.8499  | 600     | 0.0002        | -               |
| 0.9207  | 650     | 0.0001        | -               |
| 0.9915  | 700     | 0.0001        | -               |
| **1.0** | **706** | **-**         | **0.0312**      |
| 1.0623  | 750     | 0.0001        | -               |
| 1.1331  | 800     | 0.0001        | -               |
| 1.2040  | 850     | 0.0001        | -               |
| 1.2748  | 900     | 0.0           | -               |
| 1.3456  | 950     | 0.0001        | -               |
| 1.4164  | 1000    | 0.0           | -               |
| 1.4873  | 1050    | 0.0           | -               |
| 1.5581  | 1100    | 0.0           | -               |
| 1.6289  | 1150    | 0.0           | -               |
| 1.6997  | 1200    | 0.0           | -               |
| 1.7705  | 1250    | 0.0           | -               |
| 1.8414  | 1300    | 0.0001        | -               |
| 1.9122  | 1350    | 0.0           | -               |
| 1.9830  | 1400    | 0.0001        | -               |
| 2.0     | 1412    | -             | 0.0366          |
| 2.0538  | 1450    | 0.0           | -               |
| 2.1246  | 1500    | 0.0001        | -               |
| 2.1955  | 1550    | 0.0           | -               |
| 2.2663  | 1600    | 0.0           | -               |
| 2.3371  | 1650    | 0.0           | -               |
| 2.4079  | 1700    | 0.0           | -               |
| 2.4788  | 1750    | 0.0           | -               |
| 2.5496  | 1800    | 0.0           | -               |
| 2.6204  | 1850    | 0.0           | -               |
| 2.6912  | 1900    | 0.0           | -               |
| 2.7620  | 1950    | 0.0           | -               |
| 2.8329  | 2000    | 0.0           | -               |
| 2.9037  | 2050    | 0.0           | -               |
| 2.9745  | 2100    | 0.0           | -               |
| 3.0     | 2118    | -             | 0.0414          |
| 3.0453  | 2150    | 0.0           | -               |
| 3.1161  | 2200    | 0.0           | -               |
| 3.1870  | 2250    | 0.0           | -               |
| 3.2578  | 2300    | 0.0           | -               |
| 3.3286  | 2350    | 0.0           | -               |
| 3.3994  | 2400    | 0.0           | -               |
| 3.4703  | 2450    | 0.0           | -               |
| 3.5411  | 2500    | 0.0           | -               |
| 3.6119  | 2550    | 0.0           | -               |
| 3.6827  | 2600    | 0.0           | -               |
| 3.7535  | 2650    | 0.0           | -               |
| 3.8244  | 2700    | 0.0           | -               |
| 3.8952  | 2750    | 0.0           | -               |
| 3.9660  | 2800    | 0.0           | -               |
| 4.0     | 2824    | -             | 0.0366          |

* 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: 2.16.0
- 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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{"library_name": "setfit", "tags": ["setfit", "sentence-transformers", "text-classification", "generated_from_setfit_trainer"], "metrics": ["accuracy"], "widget": [{"text": "receive upi mandate collect request marg techno project private limit inr 15000.00. log google pay app authorize - axis bank"}, {"text": "sep-23 statement credit card x6343 total due : inr 5575.55 min due : inr 4811.55 due date : 08-oct-23 . pay www.kotak.com/rd/ccpymt - kotak bank"}, {"text": "< # > use otp : 8233 login turtlemintpro zck+rfoaqnm"}, {"text": "arrive today : please use otp-550041 carefully read instructions secure amazon package ( id : sptp719784310 )"}, {"text": "a/c xxx51941 credit rs 132.00 12-08-2023 - fd1186130010001148int:132.00 tax:0.00. a/c balance rs 67022.91 .please call 18002082121 query . ujjivan sfb"}], "pipeline_tag": "text-classification", "inference": true, "base_model": "sentence-transformers/all-mpnet-base-v2", "model-index": [{"name": "SetFit with sentence-transformers/all-mpnet-base-v2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "Unknown", "type": "unknown", "split": "test"}, "metrics": [{"type": "accuracy", "value": 0.9722222222222222, "name": "Accuracy"}]}]}]}
setfit safetensors mpnet sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:sentence-transformers/all-mpnet-base-v2 model-index region:us