---
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: bench:Get your food to go, find a bench, and kick back with a plate of dumplings.
- text: comparison:Frankly, when you compare what you can have here for lunch, versus
    McDs or so many other sandwich shops in the city, there is no comparison.
- text: ton:We had crawfish boiled and despite making a mess, it was a ton of fun
    and quite tasty as well.
- text: traffic noise:It is set far from the small street it's on, and there is no
    traffic noise.
- text: food:The only thing more wonderful than the food (which is exceptional) is
    the service.
metrics:
- f1_micro
- f1_macro
- precision_macro
- recall_macro
pipeline_tag: text-classification
library_name: setfit
inference: false
base_model: sentence-transformers/all-MiniLM-L6-v2
model-index:
- name: SetFit Aspect Model with sentence-transformers/all-MiniLM-L6-v2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: f1_micro
      value: 0.8516772438803264
      name: F1_Micro
    - type: f1_macro
      value: 0.8441110611976916
      name: F1_Macro
    - type: precision_macro
      value: 0.8482610861593047
      name: Precision_Macro
    - type: recall_macro
      value: 0.8409649439480325
      name: Recall_Macro
---

# 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_lg
- **SetFitABSA Aspect Model:** [ronalhung/setfit-absa-restaurants-aspect](https://huggingface.co/ronalhung/setfit-absa-restaurants-aspect)
- **SetFitABSA Polarity Model:** [ronalhung/setfit-absa-restaurants-polarity](https://huggingface.co/ronalhung/setfit-absa-restaurants-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>'staff:But the staff was so horrible to us.'</li><li>"food:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora."</li><li>"food:The food is uniformly exceptional, with a very capable kitchen which will proudly whip up whatever you feel like eating, whether it's on the menu or not."</li></ul>                                                                                                                              |
| no aspect | <ul><li>"factor:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora."</li><li>"deficiencies:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora."</li><li>"Teodora:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora."</li></ul> |

## Evaluation

### Metrics
| Label   | F1_Micro | F1_Macro | Precision_Macro | Recall_Macro |
|:--------|:---------|:---------|:----------------|:-------------|
| **all** | 0.8517   | 0.8441   | 0.8483          | 0.8410       |

## 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(
    "ronalhung/setfit-absa-restaurants-aspect",
    "ronalhung/setfit-absa-restaurants-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   | 4   | 19.4181 | 45  |

| Label     | Training Sample Count |
|:----------|:----------------------|
| no aspect | 167                   |
| aspect    | 254                   |

### Training Hyperparameters
- batch_size: (64, 64)
- 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: False
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0007 | 1    | 0.3998        | -               |
| 0.0345 | 50   | 0.3187        | 0.3072          |
| 0.0689 | 100  | 0.2744        | 0.2600          |
| 0.1034 | 150  | 0.2494        | 0.2504          |
| 0.1378 | 200  | 0.2459        | 0.2408          |
| 0.1723 | 250  | 0.2242        | 0.2210          |
| 0.2068 | 300  | 0.1802        | 0.1815          |
| 0.2412 | 350  | 0.1085        | 0.1787          |
| 0.2757 | 400  | 0.0435        | 0.1918          |
| 0.3101 | 450  | 0.0143        | 0.1832          |
| 0.3446 | 500  | 0.0063        | 0.1971          |
| 0.3790 | 550  | 0.004         | 0.1945          |
| 0.4135 | 600  | 0.002         | 0.2005          |

### Framework Versions
- Python: 3.11.13
- SetFit: 1.1.2
- Sentence Transformers: 4.1.0
- spaCy: 3.8.7
- Transformers: 4.52.4
- 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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