---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
datasets:
- Ramyashree/Dataset-500-validation
metrics:
- accuracy
widget:
- text: i want to know the status of my reimbursement, how do i track it?
- text: ask an agent how to modify my profile
- text: I want to use my other online account, help me switch them
- text: I want information about your money back policy
- text: how can I switch to another account?
pipeline_tag: text-classification
inference: true
base_model: thenlper/gte-large
---

# SetFit with thenlper/gte-large

This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [Ramyashree/Dataset-500-validation](https://huggingface.co/datasets/Ramyashree/Dataset-500-validation) 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:** 10 classes
- **Training Dataset:** [Ramyashree/Dataset-500-validation](https://huggingface.co/datasets/Ramyashree/Dataset-500-validation)
<!-- - **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                                                                                                                                                                                                                               |
|:--------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| create_account      | <ul><li>'can i register?'</li><li>'i have no account, what do i have to do?'</li><li>'i watn to know if i can register two profiles with the same email address'</li></ul>                                                             |
| delete_account      | <ul><li>'I changed my mind, what should I do to cancel my profile?'</li><li>'i changed my mind, what do i have to do to delete my account?'</li><li>"I odn't want my user account, how do I delete it?"</li></ul>                      |
| edit_account        | <ul><li>'I want to change my profile, how can I do it?'</li><li>'I need help making changes to my profile'</li><li>'can I make changes to my profile?'</li></ul>                                                                       |
| recover_password    | <ul><li>'could u ask an agent if i could retrieve my password?'</li><li>'my online account was hacked, how do I recover it?'</li><li>'my account was hacked, can u recover it?'</li></ul>                                              |
| track_refund        | <ul><li>'can yoy tell me about the status of my reimbursement?'</li><li>'tell me if my reimbursement was processed'</li><li>'I want to view the status of my refund, what can I do?'</li></ul>                                         |
| check_refund_policy | <ul><li>'I want to check your reimbursement policy, what can I do?'</li><li>'cam u ask an agent if i can see their money back guarantee?'</li><li>'I want to check your refund policy'</li></ul>                                       |
| switch_account      | <ul><li>'I weant to use my other  account, switch them'</li><li>'ask an agent if i can change to another user account'</li><li>'where to change to another profile'</li></ul>                                                          |
| payment_issue       | <ul><li>'I have a problem when trying to pay for my online order, notify it'</li><li>'could you ask an agent where I can report issues making a payment, please?'</li><li>'ask an agent how i can inform of problems paying'</li></ul> |
| get_refund          | <ul><li>'the concert was postponed and i want to get a reimbursement'</li><li>'the concert was postponed, help me get a reimbursement'</li><li>'how to get a reimbursement'</li></ul>                                                  |
| get_invoice         | <ul><li>'I want to request some bills, can you tell me how I can do it?'</li><li>'ask an agent how I can request somebills'</li><li>'i want to see a bill'</li></ul>                                                                   |

## 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("Ramyashree/gte-large-with500records-validate")
# Run inference
preds = model("how can I switch to another account?")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 3   | 10.356 | 25  |

| Label               | Training Sample Count |
|:--------------------|:----------------------|
| check_refund_policy | 50                    |
| create_account      | 50                    |
| delete_account      | 50                    |
| edit_account        | 50                    |
| get_invoice         | 50                    |
| get_refund          | 50                    |
| payment_issue       | 50                    |
| recover_password    | 50                    |
| switch_account      | 50                    |
| track_refund        | 50                    |

### 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
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0008 | 1    | 0.3184        | -               |
| 0.04   | 50   | 0.1532        | -               |
| 0.08   | 100  | 0.0078        | -               |
| 0.12   | 150  | 0.0124        | -               |
| 0.16   | 200  | 0.0017        | -               |
| 0.2    | 250  | 0.0009        | -               |
| 0.24   | 300  | 0.0008        | -               |
| 0.28   | 350  | 0.0008        | -               |
| 0.32   | 400  | 0.0007        | -               |
| 0.36   | 450  | 0.0008        | -               |
| 0.4    | 500  | 0.0004        | -               |
| 0.44   | 550  | 0.0005        | -               |
| 0.48   | 600  | 0.0004        | -               |
| 0.52   | 650  | 0.0005        | -               |
| 0.56   | 700  | 0.0003        | -               |
| 0.6    | 750  | 0.0004        | -               |
| 0.64   | 800  | 0.0003        | -               |
| 0.68   | 850  | 0.0003        | -               |
| 0.72   | 900  | 0.0003        | -               |
| 0.76   | 950  | 0.0004        | -               |
| 0.8    | 1000 | 0.0004        | -               |
| 0.84   | 1050 | 0.0004        | -               |
| 0.88   | 1100 | 0.0002        | -               |
| 0.92   | 1150 | 0.0002        | -               |
| 0.96   | 1200 | 0.0003        | -               |
| 1.0    | 1250 | 0.0004        | -               |

### 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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