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: "RT @Lrihendry: #TedCruz headed into the Presidential Debates. GO TED!! \n\
    \n#GOPDebates http://t.co/8S67pz8a4A"
- text: 'One thing in the debate was evident, apart from Trump, Rand Paul is the most
    absurd choice for a candidate. #GOPDebate'
- text: "RT @aqv21: How #Hillary Looked When Watching #CarlyFiorina #GOPDebate #Carly2016\
    \ #tcot #pjnet #ccot #tlot #RedNationRising http://t.co/aYgMâ\x80¦"
- text: 'Who do you think won the #GOPDebate last night?'
- text: '@RealAlexJones @libertytarian @JakariJax @LeeAnnMcAdoo Wether @realDonaldTrump
    is a trojan horse or not, is he worth a punt? #GOPDebate'
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: 0.5306666666666666
      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:** 3 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                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
|:---------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Positive | <ul><li>'.@JohnKasich won this debate with a little home field advantage. #GOPDebates'</li><li>'RT @Mike_Surtel: @megynkelly your questions were more like attacks on @realDonaldTrump. Then u get upset when he got tough with u! What a jâ\x80¦'</li><li>'RT @kwrcrow: Congrats to @realDonaldTrump for your win in #GOPDebates polling last night. @Time @DRUDGE_REPORT Well done Sir! http://t.co/nâ\x80¦'</li></ul>                                           |
| Neutral  | <ul><li>'RT @CharleneCac: So does his position on Iran mean that Rick Perry is also pro-divestment from Israel? #GOPDebate'</li><li>"We Watched The Debate With A Bunch Of Conservative Activists. Here's How They Reacted #GOPDebate http://t.co/Ug21fI5FcE via @dailycaller"</li><li>"I loved the cluelessness of invoking Reagan's name on #IranDeal at #GOPDebate considering Reagan made deals w/ them."</li></ul>                                            |
| Negative | <ul><li>"beeteedubs.  If you have to play 'Lesser-of-17-Evils' with your party ... perhaps you need a new party.  #p2 #tcot #GOPDebate"</li><li>"RT @Ornyadams: Single payer... no way! I would miss paying ten different bills after my annual physical. Where's the fun in writing one cheâ\x80¦"</li><li>"RT @madyclahane: srry rather not have decisions over my body being made by men that can't count to two #GOPDebate  https://t.co/1Ps81yQaOl"</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.5307   |

## 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("subham18/setfit-paraphrase-mpnet-base-v2-twitter-sentiment")
# Run inference
preds = model("Who do you think won the #GOPDebate last night?")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 8   | 18.0833 | 25  |

| Label    | Training Sample Count |
|:---------|:----------------------|
| Negative | 8                     |
| Positive | 8                     |
| Neutral  | 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 |
|:-------:|:------:|:-------------:|:---------------:|
| 0.0417  | 1      | 0.2934        | -               |
| 1.0     | 24     | -             | 0.263           |
| **2.0** | **48** | **-**         | **0.2555**      |
| 2.0833  | 50     | 0.0091        | -               |
| 3.0     | 72     | -             | 0.2598          |
| 4.0     | 96     | -             | 0.261           |

* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.12.3
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- Transformers: 4.39.0
- PyTorch: 2.4.0+cu121
- Datasets: 2.21.0
- Tokenizers: 0.15.2

## 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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