---
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Edelweiss:Downgrade Wipro to 'Hold', says Edelweiss
- text: Overweight:Morgan Stanley upgrades Axis Bank to Overweight; ups target price
- text: 'downside:Expect more downside in the IT, pharma stocks: Sandeep Wagle'
- text: 'Barclays:Infusion of additional $1 trillion to India''s GDP to create new
    midcap leaders: Barclays'
- text: focus:Jaypee, Reliance Group stocks in focus ahead of UP results
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:** [Askinkaty/setfit-finance-aspect](https://huggingface.co/Askinkaty/setfit-finance-aspect)
- **SetFitABSA Polarity Model:** [Askinkaty/setfit-finance-polarity](https://huggingface.co/Askinkaty/setfit-finance-polarity)
- **Maximum Sequence Length:** 256 tokens
- **Number of Classes:** 2 classes


### 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>'Sebi:Ponzi schemes: Sebi seeks quarterly meetings of state panels'</li><li>'Vodafone:European shares steady, pegged back by Vodafone'</li><li>'European shares:European shares steady, pegged back by Vodafone'</li></ul>                                 |
| no aspect | <ul><li>'Ponzi schemes:Ponzi schemes: Sebi seeks quarterly meetings of state panels'</li><li>'meetings:Ponzi schemes: Sebi seeks quarterly meetings of state panels'</li><li>'state panels:Ponzi schemes: Sebi seeks quarterly meetings of state panels'</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(
    "Askinkaty/setfit-finance-aspect",
    "Askinkaty/setfit-finance-polarity",
)
# Run inference
preds = model("Banking stocks to see lot of traction: Mitesh Thacker.")
```

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### Training Hyperparameters
- batch_size: 64
- num_epochs: 2
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: 2e-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


### Framework Versions
- Python: 3.11.11
- SetFit: 1.1.0
- Sentence Transformers: 3.3.1
- spaCy: 3.7.5
- Transformers: 4.42.1
- PyTorch: 2.5.1+cu124
- Datasets: 3.2.0
- Tokenizers: 0.19.1

## 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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