README.md

---
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: 0.416289592760181
      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                                                                                                                                                                                                                                                                                                                                                                        |
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 3     | <ul><li>'an indispensable peek at the art and the agony of making people laugh .'</li><li>"there 's a lot to recommend read my lips ."</li><li>'but it also has many of the things that made the first one charming .'</li></ul>                                                                                                                                                |
| 1     | <ul><li>'a baffling mixed platter of gritty realism and magic realism with a hard-to-swallow premise .'</li><li>'each scene drags , underscoring the obvious , and sentiment is slathered on top .'</li><li>'even bigger and more ambitious than the first installment , spy kids 2 looks as if it were made by a highly gifted 12-year-old instead of a grown man .'</li></ul> |
| 4     | <ul><li>'about schmidt is undoubtedly one of the finest films of the year .'</li><li>'a compelling pre-wwii drama with vivid characters and a warm , moving message .'</li><li>'twenty years later , e.t. is still a cinematic touchstone .'</li></ul>                                                                                                                          |
| 2     | <ul><li>'an unremarkable , modern action\\/comedy buddy movie whose only nod to nostalgia is in the title .'</li><li>'a movie that seems motivated more by a desire to match mortarboards with dead poets society and good will hunting than by its own story .'</li><li>"i ca n't ."</li></ul>                                                                                 |
| 0     | <ul><li>'... about as exciting to watch as two last-place basketball teams playing one another on the final day of the season .'</li><li>'... no charm , no laughs , no fun , no reason to watch .'</li><li>'this one aims for the toilet and scores a direct hit .'</li></ul>                                                                                                  |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.4163   |

## 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")
# 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   | 4   | 16.2   | 35  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 8                     |
| 1     | 8                     |
| 2     | 8                     |
| 3     | 8                     |
| 4     | 8                     |

### 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 |
|:-----:|:----:|:-------------:|:---------------:|
| 0.005 | 1    | 0.2435        | -               |
| 0.25  | 50   | 0.1137        | -               |
| 0.5   | 100  | 0.0018        | -               |
| 0.75  | 150  | 0.0049        | -               |
| 1.0   | 200  | 0.0026        | -               |

### 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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