---
library_name: setfit
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: waiter:After sitting at the table with empty glasses for a 1/2 hour, we had
    to ask the busboys to get us drinks as our waiter was nowhere to be found.
- text: presentation:The service was impeccible, the menu traditional but inventive
    and presentation for the mostpart excellent but the food itself came up short.
- text: Friday night:Without reservations on a Friday night at 8:30 I was promptly
    seated and given top-notch recommendations from both the host and my waiter.
- text: time:last time, the waiter told my roommate he'd have to charge her $5 for
    mushrooms as one of her omelette choices (never heard that at my other favorite
    brunch places.
- text: waitstaff:And the waitstaff has very little knowledge of the food, they served
    me the wrong dish and no one could identify what it was that they gave me, someone
    said pork chop, someone said lamb, and then they insisted it should be fine since
    it was the same price.
pipeline_tag: text-classification
inference: false
base_model: sentence-transformers/paraphrase-mpnet-base-v2
model-index:
- name: SetFit Aspect 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: 0.8051948051948052
      name: Accuracy
---

# 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/atsa-mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-aspect](https://huggingface.co/NazmusAshrafi/atsa-mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-aspect)
- **SetFitABSA Polarity Model:** [NazmusAshrafi/atsa-mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-polarity](https://huggingface.co/NazmusAshrafi/atsa-mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-polarity)
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 2 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
|:----------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| aspect    | <ul><li>'decor:The decor is not special at all but their food and amazing prices make up for it.'</li><li>'food:The decor is not special at all but their food and amazing prices make up for it.'</li><li>'prices:The decor is not special at all but their food and amazing prices make up for it.'</li></ul>                                                                                                                                                                                      |
| no aspect | <ul><li>'party:when tables opened up, the manager sat another party before us.'</li><li>"offerings:Though the menu includes some unorthodox offerings (a peanut butter roll, for instance), the classics are pure and great--we've never had better sushi anywhere, including Japan."</li><li>"instance:Though the menu includes some unorthodox offerings (a peanut butter roll, for instance), the classics are pure and great--we've never had better sushi anywhere, including Japan."</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.8052   |

## 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/atsa-mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-aspect",
    "NazmusAshrafi/atsa-mams-ds-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   | 7   | 29.7429 | 63  |

| Label     | Training Sample Count |
|:----------|:----------------------|
| no aspect | 115                   |
| aspect    | 130                   |

### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0005 | 1    | 0.2136        | -               |
| 0.0263 | 50   | 0.264         | -               |
| 0.0527 | 100  | 0.2717        | -               |
| 0.0790 | 150  | 0.2099        | -               |
| 0.1053 | 200  | 0.1357        | -               |
| 0.1316 | 250  | 0.1224        | -               |
| 0.1580 | 300  | 0.0305        | -               |
| 0.1843 | 350  | 0.0016        | -               |
| 0.2106 | 400  | 0.0015        | -               |
| 0.2370 | 450  | 0.0004        | -               |
| 0.2633 | 500  | 0.0006        | -               |
| 0.2896 | 550  | 0.0109        | -               |
| 0.3160 | 600  | 0.0002        | -               |
| 0.3423 | 650  | 0.0001        | -               |
| 0.3686 | 700  | 0.0001        | -               |
| 0.3949 | 750  | 0.0003        | -               |
| 0.4213 | 800  | 0.0001        | -               |
| 0.4476 | 850  | 0.0002        | -               |
| 0.4739 | 900  | 0.0001        | -               |
| 0.5003 | 950  | 0.0002        | -               |
| 0.5266 | 1000 | 0.0001        | -               |
| 0.5529 | 1050 | 0.0001        | -               |
| 0.5793 | 1100 | 0.0001        | -               |
| 0.6056 | 1150 | 0.0001        | -               |
| 0.6319 | 1200 | 0.0002        | -               |
| 0.6582 | 1250 | 0.0001        | -               |
| 0.6846 | 1300 | 0.0001        | -               |
| 0.7109 | 1350 | 0.0001        | -               |
| 0.7372 | 1400 | 0.0001        | -               |
| 0.7636 | 1450 | 0.0001        | -               |
| 0.7899 | 1500 | 0.0001        | -               |
| 0.8162 | 1550 | 0.0001        | -               |
| 0.8425 | 1600 | 0.0169        | -               |
| 0.8689 | 1650 | 0.0001        | -               |
| 0.8952 | 1700 | 0.0001        | -               |
| 0.9215 | 1750 | 0.0001        | -               |
| 0.9479 | 1800 | 0.0001        | -               |
| 0.9742 | 1850 | 0.0001        | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.4.0
- spaCy: 3.7.4
- Transformers: 4.37.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.17.1
- 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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