---
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: fast:fast boot up, great 1080p resolution, expandable (added 4gb additional
    ram and a 1tb hd) and great value for it's $365+tax price point.
- text: thinness:the sleekness and thinness of this laptop is lightweight and easy
    to carry.
- text: read:when what i heave read, the memory is not upgradeable since it's soldered
    to the board.
- text: memory:a good amount of memory. it doesnt need to have a bunch of memory,
    but a decent amount is perfect!
- text: wifi:nevertheless great processor, great graphics, 16 gb memory runs cool
    in daily use, battery lasts about 6-7 hours using wifi and video.
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: false
base_model: sentence-transformers/all-MiniLM-L6-v2
---

# SetFit Aspect Model with sentence-transformers/all-MiniLM-L6-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/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-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/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **spaCy Model:** en_core_web_sm
- **SetFitABSA Aspect Model:** [najwaa/absa-laptops-aspect](https://huggingface.co/najwaa/absa-laptops-aspect)
- **SetFitABSA Polarity Model:** [najwaa/absa-laptops-polarity](https://huggingface.co/najwaa/absa-laptops-polarity)
- **Maximum Sequence Length:** 256 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>'lightweight:very lightweight.'</li><li>'carry:this computer is so light weight and easy to carry.'</li><li>"lightweight:it's lightweight, the screen is decently bright, and it'll go for hours without needing a charge"</li></ul>                            |
| no aspect | <ul><li>'computer:this computer is so light weight and easy to carry.'</li><li>'weight:this computer is so light weight and easy to carry.'</li><li>"screen:it's lightweight, the screen is decently bright, and it'll go for hours without needing a charge"</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(
    "najwaa/absa-laptops-aspect",
    "najwaa/absa-laptops-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   | 1   | 15.9156 | 37  |

| Label     | Training Sample Count |
|:----------|:----------------------|
| no aspect | 251                   |
| aspect    | 140                   |

### Training Hyperparameters
- batch_size: (128, 128)
- num_epochs: (5, 5)
- 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: True
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0015 | 1    | 0.29          | -               |
| 0.0770 | 50   | 0.2977        | 0.2646          |
| 0.1541 | 100  | 0.2622        | 0.2558          |
| 0.2311 | 150  | 0.2493        | 0.2482          |
| 0.3082 | 200  | 0.2347        | 0.2261          |
| 0.3852 | 250  | 0.1396        | 0.1701          |
| 0.4622 | 300  | 0.0514        | 0.1434          |
| 0.5393 | 350  | 0.0227        | 0.1808          |
| 0.6163 | 400  | 0.0161        | 0.1624          |
| 0.6934 | 450  | 0.011         | 0.1718          |
| 0.7704 | 500  | 0.0101        | 0.1731          |
| 0.8475 | 550  | 0.0089        | 0.1433          |
| 0.9245 | 600  | 0.0061        | 0.1682          |
| 1.0015 | 650  | 0.0086        | 0.1627          |
| 1.0786 | 700  | 0.0078        | 0.1767          |
| 1.1556 | 750  | 0.0068        | 0.1773          |
| 1.2327 | 800  | 0.0065        | 0.1766          |

### Framework Versions
- Python: 3.11.12
- SetFit: 1.1.2
- Sentence Transformers: 4.1.0
- spaCy: 3.7.5
- Transformers: 4.51.3
- PyTorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.1

## 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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