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---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: I was charged twice for the same service on my last billing cycle. Can you
help me confirm this?
- text: Can you tell me if the ATM is available 24/7 at this location?
- text: Can you explain how my account balance affects the service fees I’m being
charged?
- text: I need to know the outstanding amount on my education loan.
- text: Can you break down how interest rates affect my monthly payments for a home
equity loan?
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
datasets:
- rbojja/labelled_bank_support_dataset
base_model: BAAI/bge-small-en-v1.5
model-index:
- name: SetFit with BAAI/bge-small-en-v1.5
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: rbojja/labelled_bank_support_dataset
type: rbojja/labelled_bank_support_dataset
split: test
metrics:
- type: accuracy
value: 0.8640988372093024
name: Accuracy
---
# SetFit with BAAI/bge-small-en-v1.5
This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [rbojja/labelled_bank_support_dataset](https://huggingface.co/datasets/rbojja/labelled_bank_support_dataset) dataset that can be used for Text Classification. This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) 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:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)
- **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:** [rbojja/labelled_bank_support_dataset](https://huggingface.co/datasets/rbojja/labelled_bank_support_dataset)
<!-- - **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 |
|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1 | <ul><li>'Can you explain the differences between fixed and variable interest rates for personal loans?'</li><li>'Can you clarify why I was charged a fee for my account this month?'</li><li>'How can I access your customer service features if I need assistance?'</li></ul> |
| 2 | <ul><li>'Could you please verify that my deposit cancellation has been completed?'</li><li>"I've noticed a discrepancy in my balance after my latest deposit. Can you confirm if it was processed correctly?"</li><li>'Could you verify that my recent payment has been reversed successfully?'</li></ul> |
| 3 | <ul><li>"How do changes in the central bank's interest rates affect the interest I earn on my savings account?"</li><li>'Can I share my success story about earning rewards for referring friends? The incentives really helped me and I think others should know!'</li><li>'After my recent loan denial, how can I strengthen my reapplication to improve my approval odds?'</li></ul> |
| 10 | <ul><li>"I just made a payment, but I'm not sure if it's been processed yet. Can you check for me?"</li><li>'I think there might be an error with my account balance; can you show me my most recent transactions?'</li><li>'I expected my payment to be completed by now, can you check the status for me?'</li></ul> |
| 9 | <ul><li>'Can you please pass on my thanks to the customer service representative who helped me with my account security concerns? Their support made all the difference!'</li><li>'I just wanted to say thank you for the quick help with my issue!'</li><li>'I want to express my appreciation for the security alerts I received. It’s nice to know my account is being protected so well!'</li></ul> |
| 4 | <ul><li>"I'm ending my account with you; can you provide a summary of my final transaction?"</li></ul> |
| 7 | <ul><li>'Can I leave a review about my downgrade process? I have some suggestions for improvement.'</li><li>'Can you send me a notification of all my completed payments for this month?'</li><li>'Can you tell me how I can benefit from any investment opportunities with your bank?'</li></ul> |
| 6 | <ul><li>'I just checked my banking statement and I don’t recognize this last charge. How can I contest that?'</li><li>"I believe I've been incorrectly charged for a subscription service. What steps do I take?"</li><li>'I appreciate the service you provide, but the support response time during the outage was unacceptable.'</li></ul> |
| 0 | <ul><li>'I’m sorry, but I need to check on a transaction that was denied because of insufficient funds. Can you help me resolve this situation?'</li><li>"I'm really sorry, but I still can't access my account even after resetting my password. What should I do next?"</li><li>"I've been mistakenly locked out of my account and I feel bad about it. Can you assist me in regaining access?"</li></ul> |
| 8 | <ul><li>'I recently used your services and I’m really satisfied. Is there a way to share my thoughts on my overall banking experience?'</li><li>'I think it would be great if final account statements could be sent out at the beginning of the month instead of the end. It allows for better planning and review.'</li><li>'I wanted to share that I found the loan application submission very straightforward. When can I expect to hear back about my approval?'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.8641 |
## 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("rbojja/ft-intent-bank")
# Run inference
preds = model("I need to know the outstanding amount on my education loan.")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 7 | 15.322 | 31 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 7 |
| 1 | 797 |
| 2 | 29 |
| 3 | 18 |
| 4 | 1 |
| 6 | 15 |
| 7 | 7 |
| 8 | 6 |
| 9 | 63 |
| 10 | 57 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 16)
- max_steps: 3450
- 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
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0003 | 1 | 0.2179 | - |
| 0.0145 | 50 | 0.2598 | - |
| 0.0290 | 100 | 0.2349 | - |
| 0.0435 | 150 | 0.2019 | - |
| 0.0580 | 200 | 0.1686 | - |
| 0.0725 | 250 | 0.1375 | - |
| 0.0870 | 300 | 0.1265 | - |
| 0.1014 | 350 | 0.0954 | - |
| 0.1159 | 400 | 0.0794 | - |
| 0.1304 | 450 | 0.065 | - |
| 0.1449 | 500 | 0.0731 | - |
| 0.1594 | 550 | 0.0547 | - |
| 0.1739 | 600 | 0.043 | - |
| 0.1884 | 650 | 0.0327 | - |
| 0.2029 | 700 | 0.027 | - |
| 0.2174 | 750 | 0.0285 | - |
| 0.2319 | 800 | 0.0201 | - |
| 0.2464 | 850 | 0.0151 | - |
| 0.2609 | 900 | 0.0131 | - |
| 0.2754 | 950 | 0.0076 | - |
| 0.2899 | 1000 | 0.0147 | - |
| 0.3043 | 1050 | 0.0122 | - |
| 0.3188 | 1100 | 0.0109 | - |
| 0.3333 | 1150 | 0.0126 | - |
| 0.3478 | 1200 | 0.0108 | - |
| 0.3623 | 1250 | 0.009 | - |
| 0.3768 | 1300 | 0.0072 | - |
| 0.3913 | 1350 | 0.0051 | - |
| 0.4058 | 1400 | 0.0057 | - |
| 0.4203 | 1450 | 0.0056 | - |
| 0.4348 | 1500 | 0.0079 | - |
| 0.4493 | 1550 | 0.0076 | - |
| 0.4638 | 1600 | 0.0029 | - |
| 0.4783 | 1650 | 0.0039 | - |
| 0.4928 | 1700 | 0.003 | - |
| 0.5072 | 1750 | 0.0037 | - |
| 0.5217 | 1800 | 0.0022 | - |
| 0.5362 | 1850 | 0.0032 | - |
| 0.5507 | 1900 | 0.0034 | - |
| 0.5652 | 1950 | 0.006 | - |
| 0.5797 | 2000 | 0.0046 | - |
| 0.5942 | 2050 | 0.0026 | - |
| 0.6087 | 2100 | 0.0031 | - |
| 0.6232 | 2150 | 0.0041 | - |
| 0.6377 | 2200 | 0.0049 | - |
| 0.6522 | 2250 | 0.0015 | - |
| 0.6667 | 2300 | 0.0053 | - |
| 0.6812 | 2350 | 0.0033 | - |
| 0.6957 | 2400 | 0.0055 | - |
| 0.7101 | 2450 | 0.0044 | - |
| 0.7246 | 2500 | 0.0036 | - |
| 0.7391 | 2550 | 0.0038 | - |
| 0.7536 | 2600 | 0.0038 | - |
| 0.7681 | 2650 | 0.0027 | - |
| 0.7826 | 2700 | 0.0028 | - |
| 0.7971 | 2750 | 0.0038 | - |
| 0.8116 | 2800 | 0.0033 | - |
| 0.8261 | 2850 | 0.0035 | - |
| 0.8406 | 2900 | 0.002 | - |
| 0.8551 | 2950 | 0.0034 | - |
| 0.8696 | 3000 | 0.0053 | - |
| 0.8841 | 3050 | 0.0035 | - |
| 0.8986 | 3100 | 0.0016 | - |
| 0.9130 | 3150 | 0.0021 | - |
| 0.9275 | 3200 | 0.0021 | - |
| 0.9420 | 3250 | 0.005 | - |
| 0.9565 | 3300 | 0.0031 | - |
| 0.9710 | 3350 | 0.0038 | - |
| 0.9855 | 3400 | 0.0029 | - |
| 1.0 | 3450 | 0.0019 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.1
- Sentence Transformers: 3.3.1
- Transformers: 4.47.1
- PyTorch: 2.5.1+cu124
- Datasets: 3.2.0
- 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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