
# SetFit with thenlper/gte-large

This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [Ramyashree/Dataset-setfit-Trainer-80records](https://huggingface.co/datasets/Ramyashree/Dataset-setfit-Trainer-80records) 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-setfit-Trainer-80records](https://huggingface.co/datasets/Ramyashree/Dataset-setfit-Trainer-80records)
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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>"I don't have an online account, what do I have to do to register?"</li><li>'can you tell me if i can regisger two accounts with a single email address?'</li><li>'I have no online account, open one, please'</li></ul>                                                           |
| edit_account        | <ul><li>'how can I modify the information on my profile?'</li><li>'can u ask an agent how to make changes to my profile?'</li><li>'I want to update the information on my profile'</li></ul>                                                                                               |
| delete_account      | <ul><li>'can I close my account?'</li><li>"I don't want my account, can you delete it?"</li><li>'how do i close my online account?'</li></ul>                                                                                                                                              |
| switch_account      | <ul><li>'I would like to use my other online account , could you switch them, please?'</li><li>'i want to use my other online account, can u change them?'</li><li>'how do i change to another account?'</li></ul>                                                                         |
| get_invoice         | <ul><li>'what can you tell me about getting some bills?'</li><li>'tell me where I can request a bill'</li><li>'ask an agent if i can obtain some bills'</li></ul>                                                                                                                          |
| get_refund          | <ul><li>'the game was postponed, help me obtain a reimbursement'</li><li>'the game was postponed, what should I do to obtain a reimbursement?'</li><li>'the concert was postponed, what should I do to request a reimbursement?'</li></ul>                                                 |
| payment_issue       | <ul><li>'i have an issue making a payment with card and i want to inform of it, please'</li><li>'I got an error message when I attempted to pay, but my card was charged anyway and I want to notify it'</li><li>'I want to notify a problem making a payment, can you help me?'</li></ul> |
| check_refund_policy | <ul><li>"I'm interested in your reimbursement polivy"</li><li>'i wanna see your refund policy, can u help me?'</li><li>'where do I see your money back policy?'</li></ul>                                                                                                                  |
| recover_password    | <ul><li>'my online account was hacked and I want tyo get it back'</li><li>"I lost my password and I'd like to retrieve  it, please"</li><li>'could u ask an agent how i can reset my password?'</li></ul>                                                                                  |
| track_refund        | <ul><li>'tell me if my refund was processed'</li><li>'I need help checking the status of my refund'</li><li>'I want to see the status of my refund, can you help me?'</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-with80records")
# Run inference
preds = model("how do i close my online account?")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 4   | 10.325 | 22  |

| Label               | Training Sample Count |
|:--------------------|:----------------------|
| check_refund_policy | 8                     |
| create_account      | 8                     |
| delete_account      | 8                     |
| edit_account        | 8                     |
| get_invoice         | 8                     |
| get_refund          | 8                     |
| payment_issue       | 8                     |
| recover_password    | 8                     |
| switch_account      | 8                     |
| track_refund        | 8                     |

### 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 |
|:-----:|:----:|:-------------:|:---------------:|
| 0.005 | 1    | 0.3449        | -               |
| 0.25  | 50   | 0.022         | -               |
| 0.5   | 100  | 0.0039        | -               |
| 0.75  | 150  | 0.0012        | -               |
| 1.0   | 200  | 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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{"library_name": "setfit", "tags": ["setfit", "sentence-transformers", "text-classification", "generated_from_setfit_trainer"], "datasets": ["Ramyashree/Dataset-setfit-Trainer-80records"], "metrics": ["accuracy"], "widget": [{"text": "I want to check your money back policy, what can I do?"}, {"text": "ask an agent if i can obtain some bills"}, {"text": "my account's been hacked, what do I have to do?"}, {"text": "the event was postponed, what do i have to do to request a reimbursement?"}, {"text": "how do i close my online account?"}], "pipeline_tag": "text-classification", "inference": true, "base_model": "thenlper/gte-large"}
setfit safetensors bert sentence-transformers text-classification generated_from_setfit_trainer dataset:Ramyashree/Dataset-setfit-Trainer-80records arxiv:2209.11055 base_model:thenlper/gte-large region:us