---
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: 0.4447963800904977
      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                                                                                                                                                                                                                                                                                                                                                                                                                                               |
|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 2     | <ul><li>"a farce of a parody of a comedy of a premise , it is n't a comparison to reality so much as it is a commentary about our knowledge of films ."</li><li>'mild , meandering teen flick .'</li><li>'wanker goths are on the loose !'</li></ul>                                                                                                                                                                                                   |
| 4     | <ul><li>"the first tunisian film i have ever seen , and it 's also probably the most good-hearted yet sensual entertainment i 'm likely to see all year ."</li><li>"the film 's intimate camera work and searing performances pull us deep into the girls ' confusion and pain as they struggle tragically to comprehend the chasm of knowledge that 's opened between them ."</li><li>'the film is small in scope , yet perfectly formed .'</li></ul> |
| 0     | <ul><li>'priggish , lethargically paced parable of renewal .'</li><li>"not at all clear what it 's trying to say and even if it were -- i doubt it would be all that interesting ."</li><li>'an unfortunate title for a film that has nothing endearing about it .'</li></ul>                                                                                                                                                                          |
| 3     | <ul><li>"you may leave the theater with more questions than answers , but darned if your toes wo n't still be tapping ."</li><li>'an alternately fascinating and frustrating documentary .'</li><li>'instead of hitting the audience over the head with a moral , schrader relies on subtle ironies and visual devices to convey point of view .'</li></ul>                                                                                            |
| 1     | <ul><li>"it 's not so much a movie as a joint promotion for the national basketball association and teenaged rap and adolescent poster-boy lil ' bow wow ."</li><li>'just the sort of lazy tearjerker that gives movies about ordinary folk a bad name .'</li><li>'well , this movie proves you wrong on both counts .'</li></ul>                                                                                                                      |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.4448   |

## 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   | 2   | 20.45  | 43  |

| 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    | 0.1967        | -               |
| 0.25  | 50   | 0.0658        | -               |
| 0.5   | 100  | 0.0883        | -               |
| 0.75  | 150  | 0.0385        | -               |
| 1.0   | 200  | 0.0112        | -               |

### 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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