---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: 'The Alavas worked themselves to the bone in the last period , and English
    and San Emeterio ( 65-75 ) had already made it clear that they were not going
    to let anyone take away what they had earned during the first thirty minutes . '
- text: 'To break the uncomfortable silence , Haney began to talk . '
- text: 'For the treatment of non-small cell lung cancer , the effects of Alimta were
    compared with those of docetaxel ( another anticancer medicine ) in one study
    involving 571 patients with locally advanced or metastatic disease who had received
    chemotherapy in the past . '
- text: 'As we all know , a few minutes before the end of the game ( that their team
    had already won ) , both players deliberately wasted time which made the referee
    show the second yellow card to both of them . '
- text: 'In contrast , patients whose cancer was affecting squamous cells had shorter
    survival times if they received Alimta . '
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: 0.1271523178807947
      name: Accuracy
---

# 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 [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) 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 [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 7 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 4     | <ul><li>'One writer , signing his letter as `` Red-blooded , balanced male , `` remarked on the `` frequency of women fainting in peals , `` and suggested that they `` settle back into their traditional role of making tea at meetings . `` '</li><li>'`` No offense intended `` , he said gently . '</li><li>"`` It 's my line of work `` , he said "</li></ul>                                                                                                                                                                                                                                                                                                                                 |
| 3     | <ul><li>"It was the most exercise we 'd had all morning and it was followed by our driving immediately to the nearest watering hole . "</li><li>'Alimta is used together with cisplatin ( another anticancer medicine ) when the cancer is unresectable ( cannot be removed by surgery alone ) and malignant ( has spread , or is likely to spread easily , to other parts of the body ) , in patients who have not received chemotherapy ( medicines for cancer ) before advanced or metastatic non-small cell lung cancer that is not affecting the squamous cells . '</li><li>'If it is , it will be treated as an operator , if it is not , it will be treated as a user function . '</li></ul> |
| 6     | <ul><li>'3 -RRB- Republican congressional representatives , because of their belief in a minimalist state , are less willing to engage in local benefit-seeking than are Democratic members of Congress . '</li><li>'The idea would be to administer to patients the growth-controlling proteins made by healthy versions of the damaged genes . '</li><li>'That is the way the system works . '</li></ul>                                                                                                                                                                                                                                                                                          |
| 0     | <ul><li>'Prior to 1932 , the pattern was nearly the opposite . '</li><li>'Never in my life have I been so frightened . '</li><li>'Then your focus will go to an input text box where you can type your function . '</li></ul>                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
| 1     | <ul><li>'Mr. Neuberger realized that , although of Italian ancestry , Mr. Mariotta still could qualify as a minority person since he was born in Puerto Rico . '</li><li>'But Dr. Vogelstein had yet to nail the identity of the gene that , if damaged , flipped a colon cell into full-blown malignancy . '</li><li>'Some found it on the screen of a personal computer . '</li></ul>                                                                                                                                                                                                                                                                                                             |
| 5     | <ul><li>"On the Right , the tone was set by Jacques Chirac , who declared in 1976 that `` 900,000 unemployed would not become a problem in a country with 2 million of foreign workers , '' and on the Left by Michel Rocard explaining in 1990 that France `` can not accommodate all the world 's misery . '' "</li><li>"But the council 's program to attract and train ringers is only partly successful , says Mr. Baldwin . "</li><li>'The scientists say that since breast cancer often strikes multiple members of certain families , the gene , when inherited in a damaged form , may predispose women to the cancer . '</li></ul>                                                        |
| 2     | <ul><li>'It explains how the Committee for Medicinal Products for Veterinary Use ( CVMP ) assessed the studies performed , to reach their recommendations on how to use the medicine . '</li><li>'US banks repay state support '</li><li>'-- In most states , increasing expenditures on education , in our current circumstances , will probably make things worse , not better . '</li></ul>                                                                                                                                                                                                                                                                                                      |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.1272   |

## 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("HelgeKn/SemEval-multi-class-6")
# Run inference
preds = model("To break the uncomfortable silence , Haney began to talk . ")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 4   | 25.0952 | 74  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 6                     |
| 1     | 6                     |
| 2     | 6                     |
| 3     | 6                     |
| 4     | 6                     |
| 5     | 6                     |
| 6     | 6                     |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (2, 2)
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0095 | 1    | 0.3696        | -               |
| 0.4762 | 50   | 0.1725        | -               |
| 0.9524 | 100  | 0.0204        | -               |
| 1.4286 | 150  | 0.0051        | -               |
| 1.9048 | 200  | 0.0037        | -               |

### Framework Versions
- Python: 3.9.13
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.36.0
- PyTorch: 2.1.1+cpu
- 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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