---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: <Question> What will the ministry of tourism do to boost the flow of tourists
    to the country during the holiday season? </Question> <Answer> Anticipating a
    surge in holiday travel, the Ministry of Tourism is rolling out a multi-pronged
    strategy to attract tourists and ensure a memorable experience. The centerpiece
    is the "Festive Wonderland" campaign, transforming major cities into enchanting
    winter scenes with illuminated streets, snow machines, and festive markets overflowing
    with local crafts and delicacies. </Answer> <Question> Was the cost of such a
    strategy announced by the ministry? </Question>
- text: <Question> How does the company offer help for parents with their children?
    </Question> <Answer> At Jack Track, we understand the importance of supporting
    our employees who are parents. We offer a range of assistance programs to help
    parents with their children. Our comprehensive benefits package includes flexible
    work schedules and remote work options, allowing parents to balance their professional
    and family responsibilities effectively. </Answer> <Question> How often can we
    work remotely? </Question>
- text: <Question> Is Store Manager considered rank 3 or rank 2? </Question> <Answer>
    In our organization's hierarchical structure, the position of Store Manager is
    considered as a Rank 2 role. </Answer> <Question> What does this level of responsibility
    typically involves? </Question>
- text: <Question> How many days off do we get during Easter? </Question> <Answer>
    During Easter, employees typically enjoy a generous 15-day break, which includes
    weekends and public holidays. This extended period allows for ample time to relax
    and celebrate the holiday season with family and friends. </Answer> <Question>
    What about Christmas? </Question>
- text: <Question> What is the highest grossing movie at the box office? </Question>
    <Answer> The highest-grossing movie at the box office is Avatar. </Answer> <Question>
    How much money did the movie make? </Question>
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: sentence-transformers/all-mpnet-base-v2
model-index:
- name: SetFit with sentence-transformers/all-mpnet-base-v2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.9347826086956522
      name: Accuracy
---

# SetFit with sentence-transformers/all-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/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-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/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 384 tokens
- **Number of Classes:** 2 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### 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>'<Question> Who was the Germany national team captain during the 2006 World cup? </Question> <Answer> Michael Ballack was the Germany national team captrain during the 2006 world cup </Answer> <Question> How old was he? </Question>'</li><li>'<Question> Who was the Germany national team captain during the 2006 World cup? </Question> <Answer> Michael Ballack was the Germany national team captrain during the 2006 world cup </Answer> <Question> Who won it back then? </Question>'</li><li>'<Question> How old was Ronaldo when he moved to Real Madrid? </Question> <Answer> Ronaldo moved to Real Madrid after leaving Inter when he was 25 years old. </Answer> <Question> What year did he leave? </Question>'</li></ul>                                                                                                                                                                                                                              |
| 0     | <ul><li>'<Question> Which ocean surrounds Antarctica? </Question> <Answer> The ocean that surrounds Antarctica is the Southern Ocean. </Answer> <Question> What challenges do scientists face when conducting research in Antarctica? </Question>'</li><li>'<Question> Name a country in Oceania. </Question> <Answer> A country in Oceania is Australia. </Answer> <Question> What are some of the popular tourist attractions in Oceania? </Question>'</li><li>"<Question> What's the significance of the Suez Canal? </Question> <Answer> The Suez Canal holds great importance as a crucial Egyptian waterway that links the Mediterranean Sea to the Red Sea. It plays a pivotal role in enhancing maritime trade and transportation between Europe and Asia, providing ships with a shorter and safer route compared to the arduous journey around the southern tip of Africa. </Answer> <Question> How has the Suez Canal impacted global trade? </Question>"</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.9348   |

## 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("<Question> What is the highest grossing movie at the box office? </Question> <Answer> The highest-grossing movie at the box office is Avatar. </Answer> <Question> How much money did the movie make? </Question>")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 14  | 44.4406 | 221 |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 240                   |
| 1     | 248                   |

### Training Hyperparameters
- batch_size: (16, 16)
- 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
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0008 | 1    | 0.5762        | -               |
| 0.0410 | 50   | 0.2742        | -               |
| 0.0820 | 100  | 0.2188        | -               |
| 0.1230 | 150  | 0.0586        | -               |
| 0.1639 | 200  | 0.0194        | -               |
| 0.2049 | 250  | 0.0028        | -               |
| 0.2459 | 300  | 0.0004        | -               |
| 0.2869 | 350  | 0.0003        | -               |
| 0.3279 | 400  | 0.0002        | -               |
| 0.3689 | 450  | 0.0001        | -               |
| 0.4098 | 500  | 0.0001        | -               |
| 0.4508 | 550  | 0.0001        | -               |
| 0.4918 | 600  | 0.0001        | -               |
| 0.5328 | 650  | 0.0006        | -               |
| 0.5738 | 700  | 0.0001        | -               |
| 0.6148 | 750  | 0.0001        | -               |
| 0.6557 | 800  | 0.0001        | -               |
| 0.6967 | 850  | 0.0001        | -               |
| 0.7377 | 900  | 0.0001        | -               |
| 0.7787 | 950  | 0.0001        | -               |
| 0.8197 | 1000 | 0.0001        | -               |
| 0.8607 | 1050 | 0.0001        | -               |
| 0.9016 | 1100 | 0.0001        | -               |
| 0.9426 | 1150 | 0.0001        | -               |
| 0.9836 | 1200 | 0.0           | -               |
| 0.0008 | 1    | 0.0           | -               |
| 0.0410 | 50   | 0.0           | -               |
| 0.0820 | 100  | 0.0003        | -               |
| 0.1230 | 150  | 0.0005        | -               |
| 0.1639 | 200  | 0.0013        | -               |
| 0.2049 | 250  | 0.0008        | -               |
| 0.2459 | 300  | 0.0           | -               |
| 0.2869 | 350  | 0.0           | -               |
| 0.3279 | 400  | 0.0           | -               |
| 0.3689 | 450  | 0.0           | -               |
| 0.4098 | 500  | 0.0           | -               |
| 0.4508 | 550  | 0.0           | -               |
| 0.4918 | 600  | 0.0           | -               |
| 0.5328 | 650  | 0.0           | -               |
| 0.5738 | 700  | 0.0           | -               |
| 0.6148 | 750  | 0.0           | -               |
| 0.6557 | 800  | 0.008         | -               |
| 0.6967 | 850  | 0.0285        | -               |
| 0.7377 | 900  | 0.012         | -               |
| 0.7787 | 950  | 0.0073        | -               |
| 0.8197 | 1000 | 0.0013        | -               |
| 0.8607 | 1050 | 0.0           | -               |
| 0.9016 | 1100 | 0.0           | -               |
| 0.9426 | 1150 | 0.0           | -               |
| 0.9836 | 1200 | 0.0013        | -               |
| 1.0246 | 1250 | 0.0013        | -               |
| 1.0656 | 1300 | 0.0           | -               |
| 1.1066 | 1350 | 0.0           | -               |
| 1.1475 | 1400 | 0.0           | -               |
| 1.1885 | 1450 | 0.0           | -               |
| 1.2295 | 1500 | 0.0           | -               |
| 1.2705 | 1550 | 0.0           | -               |
| 1.3115 | 1600 | 0.0           | -               |
| 1.3525 | 1650 | 0.0022        | -               |
| 1.3934 | 1700 | 0.0           | -               |
| 1.4344 | 1750 | 0.0           | -               |
| 1.4754 | 1800 | 0.0           | -               |
| 1.5164 | 1850 | 0.0013        | -               |
| 1.5574 | 1900 | 0.0           | -               |
| 1.5984 | 1950 | 0.0           | -               |
| 1.6393 | 2000 | 0.0           | -               |
| 1.6803 | 2050 | 0.0           | -               |
| 1.7213 | 2100 | 0.0           | -               |
| 1.7623 | 2150 | 0.0           | -               |
| 1.8033 | 2200 | 0.0           | -               |
| 1.8443 | 2250 | 0.0048        | -               |
| 1.8852 | 2300 | 0.0023        | -               |
| 1.9262 | 2350 | 0.0049        | -               |
| 1.9672 | 2400 | 0.0012        | -               |
| 2.0082 | 2450 | 0.0           | -               |
| 2.0492 | 2500 | 0.0           | -               |
| 2.0902 | 2550 | 0.0           | -               |
| 2.1311 | 2600 | 0.0           | -               |
| 2.1721 | 2650 | 0.0           | -               |
| 2.2131 | 2700 | 0.0           | -               |
| 2.2541 | 2750 | 0.0           | -               |
| 2.2951 | 2800 | 0.0           | -               |
| 2.3361 | 2850 | 0.0           | -               |
| 2.3770 | 2900 | 0.0           | -               |
| 2.4180 | 2950 | 0.0           | -               |
| 2.4590 | 3000 | 0.0           | -               |
| 2.5    | 3050 | 0.0           | -               |
| 2.5410 | 3100 | 0.0           | -               |
| 2.5820 | 3150 | 0.0           | -               |
| 2.6230 | 3200 | 0.0           | -               |
| 2.6639 | 3250 | 0.0           | -               |
| 2.7049 | 3300 | 0.0           | -               |
| 2.7459 | 3350 | 0.0           | -               |
| 2.7869 | 3400 | 0.0           | -               |
| 2.8279 | 3450 | 0.0           | -               |
| 2.8689 | 3500 | 0.0           | -               |
| 2.9098 | 3550 | 0.0007        | -               |
| 2.9508 | 3600 | 0.0           | -               |
| 2.9918 | 3650 | 0.0           | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0
- Sentence Transformers: 3.2.1
- Transformers: 4.42.2
- PyTorch: 2.5.1+cu121
- Datasets: 3.1.0
- Tokenizers: 0.19.1

## 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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