README.md

---
library_name: setfit
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: 'investors:Creating huge opportunities for investors who can see past this
    rate hike cycle. Which should be over soon. #tesla $TSLA'
- text: 'Powerwall:@TeslaSolar roof stood up to #HurricaneIan with 155mph winds and
    storm surge! This Powerwall was underwater for hours and is still working perfectly.'
- text: 'analysts: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: house:Thank you @Tesla for delivering a car to the wrong address (my house),
    blocking my driveway for hours and not allowing me to pickup my kids from school
    on such a hot day. On top of that, all your driver had to say was "call Tesla
    and tell them, I'm just a driver". https://t.co/QqMXoAT7SJ
- text: 'pitch: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.'
pipeline_tag: text-classification
inference: false
base_model: sentence-transformers/paraphrase-mpnet-base-v2
---

# SetFit Aspect 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 filtering aspect span candidates.

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 this SetFit model to filter these possible aspect span candidates.**
3. Use a 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:** [NazmusAshrafi/setfit-absa-sm-stock-tweet-aspect](https://huggingface.co/NazmusAshrafi/setfit-absa-sm-stock-tweet-aspect)
- **SetFitABSA Polarity Model:** [NazmusAshrafi/setfit-absa-sm-stock-tweet-polarity](https://huggingface.co/NazmusAshrafi/setfit-absa-sm-stock-tweet-polarity)
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 2 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
|:----------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| aspect    | <ul><li>'staff:But the staff was so horrible to us.'</li><li>'@WholeMarsBlog: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>'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></ul> |
| no aspect | <ul><li>'Tesla delivery estimates:Tesla delivery estimates are at around 364k from the analysts.'</li><li>'analysts:Tesla delivery estimates are at around 364k from the analysts.'</li><li>'@Tesla critics: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></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(
    "NazmusAshrafi/setfit-absa-sm-stock-tweet-aspect",
    "NazmusAshrafi/setfit-absa-sm-stock-tweet-polarity",
)
# 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   | 7   | 34.3860 | 54  |

| Label     | Training Sample Count |
|:----------|:----------------------|
| no aspect | 93                    |
| aspect    | 21                    |

### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0017 | 1    | 0.2658        | -               |
| 0.0868 | 50   | 0.2171        | -               |
| 0.1736 | 100  | 0.0649        | -               |
| 0.2604 | 150  | 0.0259        | -               |
| 0.3472 | 200  | 0.0802        | -               |
| 0.4340 | 250  | 0.0425        | -               |
| 0.5208 | 300  | 0.0258        | -               |
| 0.6076 | 350  | 0.0435        | -               |
| 0.6944 | 400  | 0.0793        | -               |
| 0.7812 | 450  | 0.0072        | -               |
| 0.8681 | 500  | 0.0003        | -               |
| 0.9549 | 550  | 0.0116        | -               |

### 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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