---
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
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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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