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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 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) -->
<!-- - **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 |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 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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