
# 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:** 25 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/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                                                                                                                                                                                                  |
|:---------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| do_not_qualify             | <ul><li>"Your target age group doesn't include me."</li><li>"I'm outside the age range for this."</li><li>"I'm not in the age group you're looking for."</li></ul>                                        |
| can_you_email              | <ul><li>'I prefer email, can you write to me?'</li><li>'Email is more convenient for me, can you use that?'</li><li>'Can you send me the details by email?'</li></ul>                                     |
| say_again                  | <ul><li>'Can you repeat that, please?'</li><li>'I missed that, can you say it again?'</li><li>'Could you please repeat what you just said?'</li></ul>                                                     |
| hold_a_sec                 | <ul><li>'One moment, please hold.'</li><li>'Hang on for a bit, please.'</li><li>'Just a minute, please.'</li></ul>                                                                                        |
| language_barrier           | <ul><li>'English is hard for me, ¿puedo hablar en español?'</li><li>'I struggle with English, ¿puede ser en español?'</li><li>"I'm more comfortable in Spanish, ¿podemos continuar en español?"</li></ul> |
| decline                    | <ul><li>'wrong'</li><li>'Never'</li><li>"I don't want this, thank you."</li></ul>                                                                                                                         |
| transfer_request           | <ul><li>'Can you transfer this call to your superior?'</li><li>'I need to speak with someone in charge.'</li><li>'Can I speak with your manager?'</li></ul>                                               |
| scam                       | <ul><li>"I'm skeptical, this doesn't sound right."</li><li>"I'm wary, this feels like a scam."</li><li>"Are you sure this isn't a scam?"</li></ul>                                                        |
| who_are_you                | <ul><li>"I would like to know who's calling."</li><li>"Who's calling, please?"</li><li>'Who are you and why are you calling?'</li></ul>                                                                   |
| where_did_you_get_my_info  | <ul><li>'Can you explain how you got my contact info?'</li><li>"What's the source of my details you have?"</li><li>"I didn't give you my number, where did you get it?"</li></ul>                         |
| do_not_call                | <ul><li>"Stop calling me, it's annoying!"</li><li>"I don't want to be contacted again."</li><li>"Enough calls, I'm not interested!"</li></ul>                                                             |
| where_are_you_calling_from | <ul><li>'Where are you calling from?'</li><li>'From which city or country are you calling?'</li><li>'Could you inform me of your current location?'</li></ul>                                             |
| complain_calls             | <ul><li>"Too many calls like this, it's irritating."</li><li>"I've had several calls like this, it's annoying."</li><li>"I keep getting these calls, it's too much."</li></ul>                            |
| busy                       | <ul><li>"Right now isn't good, I'm busy with something."</li><li>"I'm swamped at the moment, sorry."</li><li>"I'm busy right now, can't talk."</li></ul>                                                  |
| greetings                  | <ul><li>'Hi, how can I help you?'</li><li>'Hello, what can I help you with today?'</li><li>'Hello, yes?'</li></ul>                                                                                        |
| sorry_greeting             | <ul><li>"I'm not at my best, what do you need?"</li><li>"Sorry, it's a bad time, I'm sick."</li><li>"Not a great time, I'm dealing with a personal issue."</li></ul>                                      |
| GreetBack                  | <ul><li>'Doing well, how about yourself?'</li><li>'Pretty good, what about you?'</li><li>"Not bad, and how's it going on your end?"</li></ul>                                                             |
| calling_about              | <ul><li>'Why are you calling me?'</li><li>"What's the matter, why the call?"</li><li>'May I know the reason for your call?'</li></ul>                                                                     |
| answering_machine          | <ul><li>"Leave a message and I'll get back to you."</li><li>"You're speaking to an answering machine, leave a message."</li><li>"This is an answering machine, I'm not available."</li></ul>              |
| weather                    | <ul><li>"Sunny skies here, what's it like where you are?"</li><li>"It's a bit cloudy here, is it the same there?"</li><li>"It's warm here, what about where you are?"</li></ul>                           |
| are_you_bot                | <ul><li>'Is this a bot calling me?'</li><li>'Is this a recorded message or are you real?'</li><li>'Are you a live person or a recording?'</li></ul>                                                       |
| affirmation                | <ul><li>'yes'</li><li>"That's true, yes."</li><li>"Precisely, that's right."</li></ul>                                                                                                                    |
| not_interested             | <ul><li>"This doesn't interest me, sorry."</li><li>"This offer isn't relevant to my interests."</li><li>"Thanks, but this isn't something I need."</li></ul>                                              |
| already                    | <ul><li>"I've made this purchase before."</li><li>"This isn't new to me, I have it already."</li><li>"I've been using this for a while now."</li></ul>                                                    |
| abusibve                   | <ul><li>"This is unacceptable, I won't tolerate this!"</li><li>'I demand you stop this abusive calling!'</li><li>"Stop calling me, it's harassment!"</li></ul>                                            |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.9333   |

## 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("setfit_model_id")
# Run inference
preds = model("Give me a second, please.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 1   | 6.8375 | 13  |

| Label                      | Training Sample Count |
|:---------------------------|:----------------------|
| GreetBack                  | 9                     |
| abusibve                   | 9                     |
| affirmation                | 10                    |
| already                    | 10                    |
| answering_machine          | 8                     |
| are_you_bot                | 8                     |
| busy                       | 9                     |
| calling_about              | 8                     |
| can_you_email              | 11                    |
| complain_calls             | 11                    |
| decline                    | 10                    |
| do_not_call                | 12                    |
| do_not_qualify             | 9                     |
| greetings                  | 8                     |
| hold_a_sec                 | 8                     |
| language_barrier           | 10                    |
| not_interested             | 11                    |
| say_again                  | 12                    |
| scam                       | 9                     |
| sorry_greeting             | 9                     |
| transfer_request           | 8                     |
| weather                    | 10                    |
| where_are_you_calling_from | 9                     |
| where_did_you_get_my_info  | 11                    |
| who_are_you                | 11                    |

### Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (3, 3)
- 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    | 0.1054        | -               |
| 0.0417 | 50   | 0.1111        | -               |
| 0.0833 | 100  | 0.0798        | -               |
| 0.125  | 150  | 0.0826        | -               |
| 0.1667 | 200  | 0.0308        | -               |
| 0.2083 | 250  | 0.0324        | -               |
| 0.25   | 300  | 0.0607        | -               |
| 0.2917 | 350  | 0.0042        | -               |
| 0.3333 | 400  | 0.0116        | -               |
| 0.375  | 450  | 0.0049        | -               |
| 0.4167 | 500  | 0.0154        | -               |
| 0.4583 | 550  | 0.0158        | -               |
| 0.5    | 600  | 0.0036        | -               |
| 0.5417 | 650  | 0.001         | -               |
| 0.5833 | 700  | 0.0015        | -               |
| 0.625  | 750  | 0.0012        | -               |
| 0.6667 | 800  | 0.0009        | -               |
| 0.7083 | 850  | 0.0008        | -               |
| 0.75   | 900  | 0.0008        | -               |
| 0.7917 | 950  | 0.0014        | -               |
| 0.8333 | 1000 | 0.0005        | -               |
| 0.875  | 1050 | 0.0027        | -               |
| 0.9167 | 1100 | 0.0007        | -               |
| 0.9583 | 1150 | 0.0008        | -               |
| 1.0    | 1200 | 0.0012        | -               |
| 1.0417 | 1250 | 0.0012        | -               |
| 1.0833 | 1300 | 0.0006        | -               |
| 1.125  | 1350 | 0.0005        | -               |
| 1.1667 | 1400 | 0.0003        | -               |
| 1.2083 | 1450 | 0.0012        | -               |
| 1.25   | 1500 | 0.0006        | -               |
| 1.2917 | 1550 | 0.0008        | -               |
| 1.3333 | 1600 | 0.0008        | -               |
| 1.375  | 1650 | 0.0003        | -               |
| 1.4167 | 1700 | 0.0004        | -               |
| 1.4583 | 1750 | 0.0005        | -               |
| 1.5    | 1800 | 0.0004        | -               |
| 1.5417 | 1850 | 0.0004        | -               |
| 1.5833 | 1900 | 0.0008        | -               |
| 1.625  | 1950 | 0.0004        | -               |
| 1.6667 | 2000 | 0.0004        | -               |
| 1.7083 | 2050 | 0.0021        | -               |
| 1.75   | 2100 | 0.0004        | -               |
| 1.7917 | 2150 | 0.0002        | -               |
| 1.8333 | 2200 | 0.0006        | -               |
| 1.875  | 2250 | 0.0004        | -               |
| 1.9167 | 2300 | 0.0006        | -               |
| 1.9583 | 2350 | 0.0006        | -               |
| 2.0    | 2400 | 0.0003        | -               |
| 2.0417 | 2450 | 0.0002        | -               |
| 2.0833 | 2500 | 0.0002        | -               |
| 2.125  | 2550 | 0.0003        | -               |
| 2.1667 | 2600 | 0.0004        | -               |
| 2.2083 | 2650 | 0.0004        | -               |
| 2.25   | 2700 | 0.0005        | -               |
| 2.2917 | 2750 | 0.0005        | -               |
| 2.3333 | 2800 | 0.0005        | -               |
| 2.375  | 2850 | 0.0007        | -               |
| 2.4167 | 2900 | 0.0002        | -               |
| 2.4583 | 2950 | 0.0003        | -               |
| 2.5    | 3000 | 0.0004        | -               |
| 2.5417 | 3050 | 0.0002        | -               |
| 2.5833 | 3100 | 0.0004        | -               |
| 2.625  | 3150 | 0.0002        | -               |
| 2.6667 | 3200 | 0.0002        | -               |
| 2.7083 | 3250 | 0.0003        | -               |
| 2.75   | 3300 | 0.0002        | -               |
| 2.7917 | 3350 | 0.0002        | -               |
| 2.8333 | 3400 | 0.0003        | -               |
| 2.875  | 3450 | 0.0002        | -               |
| 2.9167 | 3500 | 0.0002        | -               |
| 2.9583 | 3550 | 0.0002        | -               |
| 3.0    | 3600 | 0.0002        | -               |

### Framework Versions
- Python: 3.10.13
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.35.0
- PyTorch: 2.1.0
- Datasets: 2.14.6
- Tokenizers: 0.14.1

## 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"], "metrics": ["accuracy"], "widget": [{"text": "Please email the information to me."}, {"text": "Give me a second, please."}, {"text": "Is it possible to talk to a higher authority?"}, {"text": "Sorry, too busy to chat right now."}, {"text": "I already own one, thanks."}], "pipeline_tag": "text-classification", "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": 0.9333333333333333, "name": "Accuracy"}]}]}]}
setfit safetensors mpnet sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:sentence-transformers/paraphrase-mpnet-base-v2 model-index region:us