setfit safetensors mpnet sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:sentence-transformers/paraphrase-mpnet-base-v2 base_model:finetune:sentence-transformers/paraphrase-mpnet-base-v2 region:us
---
base_model: sentence-transformers/paraphrase-mpnet-base-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
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 . '
inference: true
---

# 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
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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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 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>'That is the way the system works . '</li><li>'Duck swarms . '</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>'Tricks such as those of Alonso and Ramos before the Ajax demonstrate wittiness but not the will to get remove of a sanction . '</li><li>'The next day , Sunday , the hangover reminded Haney where he had been the night before . '</li></ul>                                                                                                                                                                                 |
| 3     | <ul><li>'If it is , it will be treated as an operator , if it is not , it will be treated as a user function . '</li><li>'Back in the chase car , we drove around some more , got stuck in a ditch , enlisted the aid of a local farmer to get out the trailer hitch and pull us out of the ditch . '</li><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></ul>                                                                                                                                                                         |
| 5     | <ul><li>'The discovery of a strange bacteria that can use arsenic as one of its nutrients widens the scope for finding new forms of life on Earth and possibly beyond . '</li><li>'I felt the temblor begin and glanced at the table next to mine , smiled that guilty smile and we both mouthed the words , `` Earth-quake ! `` together . '</li><li>'Already two major pharmaceutical companies , the Squibb unit of Bristol-Myers Squibb Co. and Hoffmann-La Roche Inc. , are collaborating with gene hunters to turn the anticipated cascade of discoveries into predictive tests and , maybe , new therapies . '</li></ul> |
| 0     | <ul><li>'Prior to 1932 , the pattern was nearly the opposite . '</li><li>'A minor contrast to Costa Rica , comparing the 22 players called by both countries for the friendly game today , at 3:05 pm at the National Stadium in San Jose . '</li><li>'Never in my life have I been so frightened . '</li></ul>                                                                                                                                                                                                                                                                                                                 |
| 4     | <ul><li>'`` To ring for even one service at this tower , we have to scrape , `` says Mr. Hammond , a retired water-authority worker . `` '</li><li>'It is a passion that usually stays in the tower , however . '</li><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></ul>                                                                                                                                                    |
| 1     | <ul><li>'Bribe by bribe , Mr. Sternberg and his co-author , Matthew C. Harrison Jr. , lead us along the path Wedtech traveled , from its inception as a small manufacturing company to the status of full-fledged defense contractor , entrusted with the task of producing vital equipment for the Army and Navy . '</li><li>"kalgebra 's console is useful as a calculator . "</li><li>'Then a wild thought ran circles through his clouded brain . '</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("HelgeKn/SemEval-multi-class-10")
# 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   | 28.1286 | 74  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 10                    |
| 1     | 10                    |
| 2     | 10                    |
| 3     | 10                    |
| 4     | 10                    |
| 5     | 10                    |
| 6     | 10                    |

### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0057 | 1    | 0.2488        | -               |
| 0.2857 | 50   | 0.2041        | -               |
| 0.5714 | 100  | 0.1094        | -               |
| 0.8571 | 150  | 0.0478        | -               |
| 1.1429 | 200  | 0.0378        | -               |
| 1.4286 | 250  | 0.0089        | -               |
| 1.7143 | 300  | 0.0036        | -               |
| 2.0    | 350  | 0.0029        | -               |

### 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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