---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
datasets:
- dvilasuero/banking77-topics-setfit
metrics:
- accuracy
widget:
- text: I requested a refund, and never received it. What can I do?
- text: I have a 1 euro fee on my statement.
- text: I would like an account for my children, how do I go about doing this?
- text: What do I need to do to transfer money into my account?
- text: Which country's currency do you support?
pipeline_tag: text-classification
inference: true
base_model: thenlper/gte-large
model-index:
- name: SetFit with thenlper/gte-large
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: dvilasuero/banking77-topics-setfit
      type: dvilasuero/banking77-topics-setfit
      split: test
    metrics:
    - type: accuracy
      value: 0.9230769230769231
      name: Accuracy
---

# SetFit with thenlper/gte-large

This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [dvilasuero/banking77-topics-setfit](https://huggingface.co/datasets/dvilasuero/banking77-topics-setfit) dataset that can be used for Text Classification. This SetFit model uses [thenlper/gte-large](https://huggingface.co/thenlper/gte-large) 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:** [thenlper/gte-large](https://huggingface.co/thenlper/gte-large)
- **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:** 8 classes
- **Training Dataset:** [dvilasuero/banking77-topics-setfit](https://huggingface.co/datasets/dvilasuero/banking77-topics-setfit)
<!-- - **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                                                                                                                                                                                                                                                                                          |
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 2     | <ul><li>'The  money I transferred does not show in the balance.'</li><li>'I was wondering how I could have two charges for the same item happen more than once in a 7 day period. Is there anyway I could get this corrected asap.'</li><li>'What is the source of my available funds?'</li></ul> |
| 0     | <ul><li>'Do you support the EU?'</li><li>"Can you freeze my account?  I just saw there are transactions on my account that I don't recognize.  How can I fix this?"</li><li>'Please close my account. I am unsatisfied with your service.'</li></ul>                                              |
| 5     | <ul><li>'Are you able to unblock my pin?'</li><li>'I can not find my card pin.'</li><li>'If I need a PIN for my card, where is it located?'</li></ul>                                                                                                                                             |
| 1     | <ul><li>"I can't get money out of the ATM"</li><li>'Where can I use this card at an ATM?'</li><li>'Can I use my card at any ATMs?'</li></ul>                                                                                                                                                      |
| 3     | <ul><li>'Can I get cash with this card anywhere?'</li><li>'Can you please show me where I can find the location to link my card?'</li><li>'Am I able to get a card in EU?'</li></ul>                                                                                                              |
| 6     | <ul><li>'My friends want to top up my account'</li><li>'Can I be topped up once I hit a certain balance?'</li><li>'Can you tell me why my top up was reverted?'</li></ul>                                                                                                                         |
| 7     | <ul><li>'How do I send my account money through transfer?'</li><li>'How do I transfer money to my account?'</li><li>'How can I transfer money from an outside bank?'</li></ul>                                                                                                                    |
| 4     | <ul><li>'Do you work with all fiat currencies?'</li><li>'Can I exchange to EUR?'</li><li>'Is my country supported'</li></ul>                                                                                                                                                                      |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.9231   |

## 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("HarshalBhg/gte-large-setfit-train-test2")
# Run inference
preds = model("I have a 1 euro fee on my statement.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 4   | 10.5833 | 40  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 10                    |
| 1     | 19                    |
| 2     | 28                    |
| 3     | 36                    |
| 4     | 13                    |
| 5     | 14                    |
| 6     | 15                    |
| 7     | 21                    |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- 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: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0026 | 1    | 0.3183        | -               |
| 0.1282 | 50   | 0.0614        | -               |
| 0.2564 | 100  | 0.0044        | -               |
| 0.3846 | 150  | 0.001         | -               |
| 0.5128 | 200  | 0.0008        | -               |
| 0.6410 | 250  | 0.001         | -               |
| 0.7692 | 300  | 0.0006        | -               |
| 0.8974 | 350  | 0.0012        | -               |

### 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: 2.15.0
- 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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