---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: '"e-Allahabad Journey loan application workflow?"'
- text: '"Relief Bonds redemption during OD tenure?"'
- text: '"Chief General Managers'' discretionary powers?"'
- text: '"Digital Journey e-Allahabad nominee update steps?"'
- text: '"SGB partial withdrawal during loan period?"'
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: sentence-transformers/paraphrase-mpnet-base-v2
model-index:
- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.975
      name: Accuracy
---

# SetFit with sentence-transformers/paraphrase-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/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-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/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 10 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** 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                                                                                                                                                                                                  |
|:-----------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Disclaimer                                                       | <ul><li>'"Terms of Use API restrictions?"'</li><li>'"Terms of Use age restrictions?"'</li><li>'"Disclaimer update alerts?"'</li></ul>                                                                     |
| IB Loan against Sovereign Gold Bond                              | <ul><li>'"Sovereign Jewel Bond loan margin requirements?"'</li><li>'"Sovereign Gold Bond joint holder rules?"'</li><li>'"Sovereign Jewel Bond nomination process?"'</li></ul>                             |
| Ind Advantage (Reward Program)                                   | <ul><li>'"Advantage Rewards international redemption fees?"'</li><li>'"Blackout dates for reward travel bookings?"'</li><li>'"Advantage Program customer support channels?"'</li></ul>                    |
| Amalgamation                                                     | <ul><li>'"Merger documentation checklist for branches?"'</li><li>'"Banking Amalgamation customer notification process?"'</li><li>'"Amalgamation loan portfolio transfer details?"'</li></ul>              |
| Loan / OD against NSC / KVP / Relief bonds of RBI / LIC policies | <ul><li>'"Relief Bonds OD interest payment frequency?"'</li><li>'"KVP valuation for overdraft approval criteria?"'</li><li>'"NSC loan documentation checklist?"'</li></ul>                                |
| Chief General Managers                                           | <ul><li>'"Chief General Managers\' office working hours?"'</li><li>'"How to contact Chief General Managers for escalations?"'</li><li>'"Senior General Managers\' regional jurisdiction list?"'</li></ul> |
| Point of Sale (PoS)                                              | <ul><li>'"Offline PoS transaction capabilities?"'</li><li>'"PoS transaction audit trails?"'</li><li>'"PoS batch settlement timing?"'</li></ul>                                                            |
| Featured Products / Services / Schemes                           | <ul><li>'"Highlighted Products insurance coverage details?"'</li><li>'"Highlighted Products loan-to-value ratio?"'</li><li>'"Featured schemes disbursement timeline?"'</li></ul>                          |
| e-Allahabad Bank Journey                                         | <ul><li>'"e-Allahabad Experience customer support channels?"'</li><li>'"Allahabad Online Journey QR code payments?"'</li><li>'"Allahabad Online Journey statement download process?"'</li></ul>           |
|  Centralized Pension Processing Centre                           | <ul><li>'"Processing time for pension applications?"'</li><li>'"QR code payments at Payment Office?"'</li><li>'"Central Pension Management Centre contact details?"'</li></ul>                            |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.975    |

## 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("kneau007/my-classifier")
# Run inference
preds = model("\"Relief Bonds redemption during OD tenure?\"")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 3   | 5.2062 | 8   |

| Label                                                            | Training Sample Count |
|:-----------------------------------------------------------------|:----------------------|
| Amalgamation                                                     | 14                    |
| Chief General Managers                                           | 16                    |
| Disclaimer                                                       | 11                    |
| Featured Products / Services / Schemes                           | 18                    |
| IB Loan against Sovereign Gold Bond                              | 18                    |
| Ind Advantage (Reward Program)                                   | 19                    |
| Loan / OD against NSC / KVP / Relief bonds of RBI / LIC policies | 16                    |
| Point of Sale (PoS)                                              | 16                    |
| e-Allahabad Bank Journey                                         | 15                    |
|  Centralized Pension Processing Centre                           | 17                    |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- 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: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0025 | 1    | 0.172         | -               |
| 0.125  | 50   | 0.1198        | -               |
| 0.25   | 100  | 0.0251        | -               |
| 0.375  | 150  | 0.0068        | -               |
| 0.5    | 200  | 0.003         | -               |
| 0.625  | 250  | 0.0018        | -               |
| 0.75   | 300  | 0.0015        | -               |
| 0.875  | 350  | 0.0013        | -               |
| 1.0    | 400  | 0.0013        | -               |

### Framework Versions
- Python: 3.11.11
- SetFit: 1.1.1
- Sentence Transformers: 3.4.1
- Transformers: 4.48.3
- PyTorch: 2.5.1+cu124
- Datasets: 3.3.2
- Tokenizers: 0.21.0

## 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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