---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: we get some truly unique character studies and a cross-section of americana
    that hollywood could n't possibly fictionalize and be believed .
- text: the movie is one of the best examples of artful large format filmmaking you
    are likely to see anytime soon .
- text: my response to the film is best described as lukewarm .
- text: the movie 's ripe , enrapturing beauty will tempt those willing to probe its
    inscrutable mysteries .
- text: fear dot com is so rambling and disconnected it never builds any suspense
    .
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: 0.5380090497737556
      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 [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:** 5 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0     | <ul><li>"it 's not a motion picture ; it 's an utterly static picture ."</li><li>"frankly , it 's kind of insulting , both to men and women ."</li><li>'under-rehearsed and lifeless'</li></ul>                                                                                                                                                                                                                                                                               |
| 2     | <ul><li>"recoing 's fantastic performance does n't exactly reveal what makes vincent tick , but perhaps any definitive explanation for it would have felt like a cheat ."</li><li>"do n't expect any subtlety from this latest entry in the increasingly threadbare gross-out comedy cycle ."</li><li>"merry friggin ' christmas !"</li></ul>                                                                                                                                 |
| 3     | <ul><li>"so purely enjoyable that you might not even notice it 's a fairly straightforward remake of hollywood comedies such as father of the bride ."</li><li>"what saves this deeply affecting film from being merely a collection of wrenching cases is corcuera 's attention to detail ."</li><li>'for once , a movie does not proclaim the truth about two love-struck somebodies , but permits them time and space to convince us of that all on their own .'</li></ul> |
| 1     | <ul><li>"the fact that it is n't very good is almost beside the point ."</li><li>'what starts off as a satisfying kids flck becomes increasingly implausible as it races through contrived plot points .'</li><li>'the film is ultimately about as inspiring as a hallmark card .'</li></ul>                                                                                                                                                                                  |
| 4     | <ul><li>'cool gadgets and creatures keep this fresh .'</li><li>'morton deserves an oscar nomination .'</li><li>'a brutal and funny work .'</li></ul>                                                                                                                                                                                                                                                                                                                          |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.5380   |

## 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("vidhi0206/setfit-paraphrase-mpnet-sst5_v2")
# Run inference
preds = model("my response to the film is best described as lukewarm .")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 2   | 18.8062 | 52  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 64                    |
| 1     | 64                    |
| 2     | 64                    |
| 3     | 64                    |
| 4     | 64                    |

### Training Hyperparameters
- batch_size: (8, 8)
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0006 | 1    | 0.2259        | -               |
| 0.0312 | 50   | 0.2373        | -               |
| 0.0625 | 100  | 0.1726        | -               |
| 0.0938 | 150  | 0.1607        | -               |
| 0.125  | 200  | 0.1869        | -               |
| 0.1562 | 250  | 0.1863        | -               |
| 0.1875 | 300  | 0.224         | -               |
| 0.2188 | 350  | 0.1625        | -               |
| 0.25   | 400  | 0.1284        | -               |
| 0.2812 | 450  | 0.1357        | -               |
| 0.3125 | 500  | 0.2193        | -               |
| 0.3438 | 550  | 0.1434        | -               |
| 0.375  | 600  | 0.0524        | -               |
| 0.4062 | 650  | 0.0558        | -               |
| 0.4375 | 700  | 0.072         | -               |
| 0.4688 | 750  | 0.0312        | -               |
| 0.5    | 800  | 0.0732        | -               |
| 0.5312 | 850  | 0.0117        | -               |
| 0.5625 | 900  | 0.0311        | -               |
| 0.5938 | 950  | 0.0228        | -               |
| 0.625  | 1000 | 0.0026        | -               |
| 0.6562 | 1050 | 0.0196        | -               |
| 0.6875 | 1100 | 0.0017        | -               |
| 0.7188 | 1150 | 0.0067        | -               |
| 0.75   | 1200 | 0.0029        | -               |
| 0.7812 | 1250 | 0.0041        | -               |
| 0.8125 | 1300 | 0.0006        | -               |
| 0.8438 | 1350 | 0.0022        | -               |
| 0.875  | 1400 | 0.0006        | -               |
| 0.9062 | 1450 | 0.0007        | -               |
| 0.9375 | 1500 | 0.001         | -               |
| 0.9688 | 1550 | 0.0009        | -               |
| 1.0    | 1600 | 0.0013        | -               |

### Framework Versions
- Python: 3.8.10
- SetFit: 1.0.3
- Sentence Transformers: 2.3.1
- Transformers: 4.37.2
- PyTorch: 2.2.0+cu121
- Datasets: 2.17.0
- Tokenizers: 0.15.1

## 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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