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 model-index 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: i miss our talks our cuddling our kissing and the feelings that you can only
    share with your beloved
- text: i feel that i m so pathetic and downright dumb to let people in let them toy
    with my feelings and then leaving me to clean up this pile of sadness inside me
- text: i told her that i woke up feeling mad that i am a woman and that i am probably
    always going to have to worry about being raped
- text: i try to share what i bake with a lot of people is because i love people and
    i want them to feel loved
- text: i feel for you despite the bitterness and longing
inference: true
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: 0.45842105263157895
      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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
|:---------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| sadness  | <ul><li>'i am from new jersey and this first drink was consumed at a post prom party so i feel it s appropriately lame'</li><li>'i am the one feeling punished'</li><li>'i wouldn t feel submissive which has it s place but not in the work environment'</li></ul>                                                                                                                                                                                                                          |
| love     | <ul><li>'i would rather take my chances on keeping my heart and getting it broken again and again then to stop feeling to stop caring to be bitter cross cynical'</li><li>'i still love to run and plan to keep it up but i don t want to once again register for so many races that i feel like every exercise moment needs to be devoted to running'</li><li>'i suddenly feel that this is more than a sweet love song that every girls could sing in front of their boyfriends'</li></ul> |
| surprise | <ul><li>'i was feeling an act of god at work in my life and it was an amazing feeling'</li><li>'i tween sat for my moms boss year old and year old boys this weekend id say babysit but that feels weird considering there were n'</li><li>'i started feeling funny and then friday i woke up sick as a dog'</li></ul>                                                                                                                                                                       |
| anger    | <ul><li>'i could of course go on with it feeling resentful of him with him being blissfully unaware of anything being wrong'</li><li>'i feel tortured because i am not allowed to enjoy food the way my friend can'</li><li>'i feel like i should be offended but yawwwn'</li></ul>                                                                                                                                                                                                          |
| joy      | <ul><li>'i was feeling over eager and hopped on to the tube to ride the eye of london'</li><li>'i am not feeling particularly creative'</li><li>'i woke on saturday feeling a little brighter and was very keen to get outdoors after spending all day friday wallowing in self pity'</li></ul>                                                                                                                                                                                              |
| fear     | <ul><li>'im feeling pretty shaken at the moment'</li><li>'i know he is totally trainable and can be free of his arm chewing habits i feel that the kids would be too nervous around him during the training process'</li><li>'i am feeling pretty restless right now while typing this'</li></ul>                                                                                                                                                                                            |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.4584   |

## 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("dendimaki/apeiron-v4")
# Run inference
preds = model("i feel for you despite the bitterness and longing")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 4   | 17.6458 | 55  |

| Label    | Training Sample Count |
|:---------|:----------------------|
| sadness  | 8                     |
| joy      | 8                     |
| love     | 8                     |
| anger    | 8                     |
| fear     | 8                     |
| surprise | 8                     |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (4, 4)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- 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: True

### Training Results
| Epoch   | Step    | Training Loss | Validation Loss |
|:-------:|:-------:|:-------------:|:---------------:|
| 0.0083  | 1       | 0.2802        | -               |
| 0.4167  | 50      | 0.1302        | -               |
| 0.8333  | 100     | 0.0121        | -               |
| 1.0     | 120     | -             | 0.2668          |
| 1.25    | 150     | 0.003         | -               |
| 1.6667  | 200     | 0.0007        | -               |
| **2.0** | **240** | **-**         | **0.2562**      |
| 2.0833  | 250     | 0.0008        | -               |
| 2.5     | 300     | 0.0009        | -               |
| 2.9167  | 350     | 0.0007        | -               |
| 3.0     | 360     | -             | 0.2572          |
| 3.3333  | 400     | 0.0005        | -               |
| 3.75    | 450     | 0.0005        | -               |
| 4.0     | 480     | -             | 0.2571          |

* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.16.0
- 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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