---
library_name: setfit
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: ; got my car in about a:Well.. I added a new 🐎 to the stable! Special thanks
    to Matt at the @Tesla Clarkston location who made my Model Y order & delivery
    incredibly smooth.🙏 I'm super lucky & got my car in about a week of deciding
    to go for it 😳 Video coming soon about that process & more! https://t.co/PrP91xMnKk
- text: '. But the price could be cheaper:C’mon @elonmusk! Australians are busting
    to buy EVs & the best one is @Tesla imho. But the price could be cheaper,
    if you built a #gigafactory in Australia. 70% of the lithium in the cars is #aussie
    so why not set up a #gigafactorydownunder? All the talent and minerals are here!'
- text: 'generate more net profit from legacy auto:As with previous quarters, $TSLA
    will generate more net profit from legacy auto regulatory credits sales this quarter
    than legacy auto will make in gross profit by selling EVs.


    This just keeps adding insult to injury.'
- text: on keeping this car for 10 years:@_brivnii @Tesla I plan on keeping this car
    for 10 years total (so 6 more years at least). I don't feel the need to upgrade
    to a newer model even if price is no issue. This one has been reliable, and I
    got a good battery (no signs of degradation so far)
- text: "The driver’s car was a @Tesla:I took an @Uber home from the airport and my\
    \ bill had a fuel surcharge on it because of the current price of gasoline. \n\
    \nThe driver’s car was a @Tesla… \U0001F937"
pipeline_tag: text-classification
inference: false
base_model: sentence-transformers/paraphrase-mpnet-base-v2
model-index:
- name: SetFit Polarity Model 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: 0.44
      name: Accuracy
---

# 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:** [NazmusAshrafi/setfit-MiniLM-mpnet-absa-tesla-tweet-aspect](https://huggingface.co/NazmusAshrafi/setfit-MiniLM-mpnet-absa-tesla-tweet-aspect)
- **SetFitABSA Polarity Model:** [NazmusAshrafi/setfit-MiniLM-mpnet-absa-tesla-tweet-polarity](https://huggingface.co/NazmusAshrafi/setfit-MiniLM-mpnet-absa-tesla-tweet-polarity)
- **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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
|:---------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| neutral  | <ul><li>"i.e. just more profit for $TSLA:I'm pretty sure, all an EV tax incentive will do, is raise the price of Teslas, at least for the next few years.\n\ni.e. just more profit for $TSLA\nAs if demand wasn't abundant enough already."</li><li>"increase also increase profit and Tesla the:C'mon @SaraEisen you know as well as I do that the price increases in $TSLA vehicles is not related to any weakness in the stock today. It's purely macro today. Plus, price increase also increase profit and Tesla the only Auto maker that is making high margins on sales."</li><li>"when choosing a car. They just:The key thing people get wrong when thinking EV competition is bad for Tesla:\n\nVast majority of consumers aren't prioritizing the environment when choosing a car. They just want the best product &amp; technology, which Tesla offers and just happens to be an EV\n\n$TSLA https://t.co/w3cKqeJkQW"</li></ul> |
| negative | <ul><li>"is raise the price of Teslas,:I'm pretty sure, all an EV tax incentive will do, is raise the price of Teslas, at least for the next few years.\n\ni.e. just more profit for $TSLA\nAs if demand wasn't abundant enough already."</li><li>'"The price of batteries for:"The price of batteries for electric vehicles looks set to rise in 2022 after many years of sharp decline. The supplies of lithium and other raw materials fail to keep up with huge demand." $NIO $TSLA $XPEV $LI\n\nhttps://t.co/2CAJCxTC2C'</li><li>'. But the price could be cheaper:C’mon @elonmusk! Australians are busting to buy EVs &amp; the best one is @Tesla imho. But the price could be cheaper, if you built a #gigafactory in Australia. 70% of the lithium in the cars is #aussie so why not set up a #gigafactorydownunder? All the talent and minerals are here!'</li></ul>                                                              |
| positive | <ul><li>'a $30k car with $70k:John Hennessey gets a $TSLA Plaid. \nA retired OEM executive describes Tesla as a $30k car with $70k in batteries. \nThe perfect description of a Tesla https://t.co/m5J5m3AuMJ'</li><li>"want the best product &amp;:The key thing people get wrong when thinking EV competition is bad for Tesla:\n\nVast majority of consumers aren't prioritizing the environment when choosing a car. They just want the best product &amp; technology, which Tesla offers and just happens to be an EV\n\n$TSLA https://t.co/w3cKqeJkQW"</li><li>"the most important product on Earth;:Tesla's 4680 battery (and it's manufactuing process) will end up being the most important product on Earth; it's that important. It will enable massive scale &amp; cost reductions over time. It will enable faster charging times &amp; longer range EVs, both will drive adoption.\n\n$TSLA"</li></ul>                        |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.44     |

## 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-MiniLM-mpnet-absa-tesla-tweet-aspect",
    "NazmusAshrafi/setfit-MiniLM-mpnet-absa-tesla-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   | 26  | 46.2121 | 61  |

| Label    | Training Sample Count |
|:---------|:----------------------|
| negative | 11                    |
| neutral  | 12                    |
| positive | 10                    |

### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0217 | 1    | 0.186         | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.2.2
- spaCy: 3.6.1
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.16.1
- Tokenizers: 0.15.1

## 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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