---
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: went to the chefs.:All the money went into the interior decoration, none of
    it went to the chefs.
- text: the little 'perks' were great:Not only was the food outstanding, but the little
    'perks' were great.
- text: for lunch or dinner.:This is a consistently great place to dine for lunch
    or dinner.
- text: such a delicious meal for such little:I just wonder how you can have such
    a delicious meal for such little money.
- text: Three courses - choices include:Three courses - choices include excellent
    mussels, puff pastry goat cheese and salad with a delicious dressing, and a hanger
    steak au poivre that is out of this world.
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: false
base_model: WhereIsAI/UAE-Large-V1
---

# SetFit Polarity Model with WhereIsAI/UAE-Large-V1

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses [WhereIsAI/UAE-Large-V1](https://huggingface.co/WhereIsAI/UAE-Large-V1) 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:** [WhereIsAI/UAE-Large-V1](https://huggingface.co/WhereIsAI/UAE-Large-V1)
- **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:** [hanshan1988/setfit-absa-UAE-Large-V1-restaurants-aspect](https://huggingface.co/hanshan1988/setfit-absa-UAE-Large-V1-restaurants-aspect)
- **SetFitABSA Polarity Model:** [hanshan1988/setfit-absa-UAE-Large-V1-restaurants-polarity](https://huggingface.co/hanshan1988/setfit-absa-UAE-Large-V1-restaurants-polarity)
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 4 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
|:---------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| negative | <ul><li>'But the staff was so horrible:But the staff was so horrible to us.'</li><li>', forgot our toast, left out:They did not have mayonnaise, forgot our toast, left out ingredients (ie cheese in an omelet), below hot temperatures and the bacon was so over cooked it crumbled on the plate when you touched it.'</li><li>'did not have mayonnaise, forgot our:They did not have mayonnaise, forgot our toast, left out ingredients (ie cheese in an omelet), below hot temperatures and the bacon was so over cooked it crumbled on the plate when you touched it.'</li></ul>                                          |
| positive | <ul><li>"factor was the food, which was: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>"The food is uniformly exceptional: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><li>"a very capable kitchen which will proudly: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> |
| neutral  | <ul><li>"'s on the menu or not.: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><li>'to sample both meats).:Our agreed favorite is the orrechiete with sausage and chicken (usually the waiters are kind enough to split the dish in half so you get to sample both meats).'</li><li>'to split the dish in half so:Our agreed favorite is the orrechiete with sausage and chicken (usually the waiters are kind enough to split the dish in half so you get to sample both meats).'</li></ul>                   |
| conflict | <ul><li>'The food was delicious but:The food was delicious but do not come here on a empty stomach.'</li><li>"The service varys from day:The service varys from day to day- sometimes they're very nice, and sometimes not."</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(
    "hanshan1988/setfit-absa-UAE-Large-V1-restaurants-aspect",
    "hanshan1988/setfit-absa-UAE-Large-V1-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   | 6   | 21.3594 | 43  |

| Label    | Training Sample Count |
|:---------|:----------------------|
| conflict | 2                     |
| negative | 19                    |
| neutral  | 25                    |
| positive | 82                    |

### 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
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0018 | 1    | 0.1974        | -               |
| 0.0923 | 50   | 0.1656        | -               |
| 0.1845 | 100  | 0.0308        | -               |
| 0.2768 | 150  | 0.0172        | -               |
| 0.3690 | 200  | 0.008         | -               |
| 0.4613 | 250  | 0.0042        | -               |
| 0.5535 | 300  | 0.0029        | -               |
| 0.6458 | 350  | 0.0018        | -               |
| 0.7380 | 400  | 0.0015        | -               |
| 0.8303 | 450  | 0.0012        | -               |
| 0.9225 | 500  | 0.0011        | -               |

### Framework Versions
- Python: 3.11.11
- SetFit: 1.1.1
- Sentence Transformers: 3.4.1
- spaCy: 3.7.5
- Transformers: 4.48.3
- PyTorch: 2.5.1+cu124
- Datasets: 3.3.0
- Tokenizers: 0.21.0

## 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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