README.md

---
library_name: setfit
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: 'feel the most confidence in is $:Ron Barron: We see so much potential, we
    don’t want to sell; Of all companies I cover & analysts come pitch to me,
    the company I feel the most confidence in is $TSLA; People think we''re going
    into a slowdown but demand for their cars has never been better.'
- text: 'surge! This Powerwall was underwater for:@TeslaSolar roof stood up to #HurricaneIan
    with 155mph winds and storm surge! This Powerwall was underwater for hours and
    is still working perfectly.'
- text: 'Guilty of overtrading in this aggressive:Guilty of overtrading in this aggressive
    price action. I’m far from perfect but I try my best to keep my losses small. '
- text: 'Creating huge opportunities for investors who:Creating huge opportunities
    for investors who can see past this rate hike cycle. Which should be over soon.
    #tesla $TSLA'
- text: 'Investing in the stock market was and never:Investing in the stock market
    was and never will be easy bc many throw in the white towel along the way, bc
    they panic. '
pipeline_tag: text-classification
inference: false
base_model: sentence-transformers/paraphrase-mpnet-base-v2
---

# SetFit Polarity Model with sentence-transformers/paraphrase-mpnet-base-v2

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). 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. In particular, this model is in charge of classifying aspect polarities.

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.

This model was trained within the context of a larger system for ABSA, which looks like so:

1. Use a spaCy model to select possible aspect span candidates.
2. Use a SetFit model to filter these possible aspect span candidates.
3. **Use this SetFit model to classify the filtered aspect span candidates.**

## 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
- **spaCy Model:** en_core_web_lg
- **SetFitABSA Aspect Model:** [setfit-absa-aspect](https://huggingface.co/setfit-absa-aspect)
- **SetFitABSA Polarity Model:** [NazmusAshrafi/setfit-absa-sm-stock-tweet-sentiment](https://huggingface.co/NazmusAshrafi/setfit-absa-sm-stock-tweet-sentiment)
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 4 classes
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### 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
|:---------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| negative | <ul><li>'But the staff was so horrible:But the staff was so horrible to us.'</li><li>'For years @WholeMarsBlog viciously silenced @Tesla:For years @WholeMarsBlog viciously silenced @Tesla critics. Failing to silence me, he desperately lashes out  with childish insults about me, my company, my products - and even my His fear and impotence spurs me on to ensure that everyone understands Full Self-Driving is Apple.'</li><li>"$NIO just because I:$NIO just because I'm down money doesn't mean this is a bad investment. The whole market, everything sucks right now. 2-5 years from now, I'm confident it will pay off."</li></ul>                                                                                                                                                 |
| neutral  | <ul><li>'-Driving is Apple.:For years @WholeMarsBlog viciously silenced @Tesla critics. Failing to silence me, he desperately lashes out  with childish insults about me, my company, my products - and even my His fear and impotence spurs me on to ensure that everyone understands Full Self-Driving is Apple.'</li><li>"adopt California's rules approved in August:New York state plans to adopt California's rules approved in August that would require all new vehicles sold in the state by 2035 to be either electric or plug-in electric hybrids."</li><li>"plug-in electric hybrids.:New York state plans to adopt California's rules approved in August that would require all new vehicles sold in the state by 2035 to be either electric or plug-in electric hybrids."</li></ul> |
| positive | <ul><li>'day! #Tesla #hawaii $:This makes my day! #Tesla #hawaii $TSLA'</li><li>'@TeslaSolar roof stood up:@TeslaSolar roof stood up to #HurricaneIan with 155mph winds and storm surge! This Powerwall was underwater for hours and is still working perfectly.'</li><li>'surge! This Powerwall was underwater for:@TeslaSolar roof stood up to #HurricaneIan with 155mph winds and storm surge! This Powerwall was underwater for hours and is still working perfectly.'</li></ul>                                                                                                                                                                                                                                                                                                              |
| neutral  | <ul><li>'Investing in the stock market was and never:Investing in the stock market was and never will be easy bc many throw in the white towel along the way, bc they panic. '</li></ul>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |

## 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 AbsaModel

# Download from the 🤗 Hub
model = AbsaModel.from_pretrained(
    "setfit-absa-aspect",
    "NazmusAshrafi/setfit-absa-sm-stock-tweet-sentiment",
)
# Run inference
preds = model("The food was great, but the venue is just way too busy.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 10  | 33.3333 | 60  |

| Label    | Training Sample Count |
|:---------|:----------------------|
| negative | 7                     |
| neutral  | 5                     |
| neutral  | 1                     |
| positive | 8                     |

### Training Hyperparameters
- batch_size: (16, 2)
- num_epochs: (1, 16)
- 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: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0526 | 1    | 0.1621        | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.2
- Sentence Transformers: 2.2.2
- spaCy: 3.6.1
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.16.1
- 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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