---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: i honestly thought impossible at this point i feel pretty
- text: i feel convinced that im going to shy away from whatever is really good for
    me
- text: i feel guilt that i should be more caring and im not
- text: i found myself feeling nostalgic as i thought about the temporarily abandoned
    little bishop chronicles
- text: i am feeling very indecisive and spontaneous
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: 0.442
      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:** 6 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>'im fine mary anne answered feeling a little impatient'</li><li>'i feel outraged that my life is so easy so blessed'</li><li>'i suggested greys and blues with warm tones as the room is north facing and could feel quite cold and flat'</li></ul>                                                                                                                                                                                                                                                                                                      |
| 4     | <ul><li>'i still feel its a little shaky at times and can move into the slightly odd jades hair in particular seems prone to this but generally it works well with spencers writing'</li><li>'i also feel strange that by the ripe old age of twenty three i want a goddamn life partner'</li><li>'i feel acclimated like i am finally a part of this organization rather than a timid observer'</li></ul>                                                                                                                                                       |
| 5     | <ul><li>'i always feel so flattered when another amazing blogger asks me to share a little of world on their blog so here it goes'</li><li>'i was like oh thats awesome blah but then he was like reminding me hes interested in this other girl and i was like i know this but what concerns me more is if it makes you feel too weird to be with me like this'</li><li>'i gotta say im feeling pretty impressed with how everything ended up considering my total dollars dropped totaled and i have three small canvases to play with display with'</li></ul> |
| 1     | <ul><li>'i still am not able to remember a single dull moment a detail that pissed me off a thing i didnt feel comfortable about'</li><li>'i can t find anything to feel other than complacent'</li><li>'i am feeling a little bouncy right now'</li></ul>                                                                                                                                                                                                                                                                                                       |
| 0     | <ul><li>'i hide this secret inside of me away from everyone because i feel ashamed and like i have no assistance in making it better'</li><li>'i could feel how exhausted my arms and legs were'</li><li>'i am also feeling awful'</li></ul>                                                                                                                                                                                                                                                                                                                     |
| 2     | <ul><li>'i can feel passionate about taking a stand and maybe understand that this one as yet to be chosen issue is worthy of my time and efforts'</li><li>'i do awaken from a mild night sweat i usually feel hot as if i had a fever and i want to remove some of my blankets'</li><li>'i am feeling quite fond of my friends'</li></ul>                                                                                                                                                                                                                       |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.442    |

## 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-emotion")
# Run inference
preds = model("i am feeling very indecisive and spontaneous")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 4   | 18.7292 | 46  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 8                     |
| 1     | 8                     |
| 2     | 8                     |
| 3     | 8                     |
| 4     | 8                     |
| 5     | 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0042 | 1    | 0.2264        | -               |
| 0.2083 | 50   | 0.0997        | -               |
| 0.4167 | 100  | 0.0257        | -               |
| 0.625  | 150  | 0.0089        | -               |
| 0.8333 | 200  | 0.003         | -               |

### 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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