---
library_name: setfit
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: BAAI/bge-small-en-v1.5
metrics:
- accuracy
widget:
- text: to be a nightmare, said retired:The Type 096s are going to be a nightmare,
    said retired submariner and naval technical intelligence analyst Christopher Carlson,
    one of the researchers.
- text: census as an independent exercise.:In fact, the government of Bihar has recently
    taken up the caste census as an independent exercise.
- text: to Moscow's Improved Akula boats.:Carlson told Reuters he did not believe
    China had obtained Russia's 'crown jewels' - its very latest technology - but
    would be producing a submarine stealthy enough to compare to Moscow's Improved
    Akula boats.
- text: staging fully armed nuclear deterrence patrols with its older:The Chinese
    navy is routinely staging fully armed nuclear deterrence patrols with its older
    Type 094 boats out of Hainan Island in the South China Sea, the Pentagon said
    in November, much like patrols operated for years by the United States, Britain,
    Russia, and France.
- text: Sanjeev Chopra is a former:Sanjeev Chopra is a former IAS officer and Festival
    Director of Valley of Words.
pipeline_tag: text-classification
inference: false
model-index:
- name: SetFit Polarity Model with BAAI/bge-small-en-v1.5
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.8387096774193549
      name: Accuracy
---

# SetFit Polarity Model with BAAI/bge-small-en-v1.5

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) 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:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)
- **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:** [asadnaqvi/setfitabsa-aspect](https://huggingface.co/asadnaqvi/setfitabsa-aspect)
- **SetFitABSA Polarity Model:** [asadnaqvi/setfitabsa-polarity](https://huggingface.co/asadnaqvi/setfitabsa-polarity)
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 4 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
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### 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
|:------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Informative | <ul><li>"The upcoming visit of Saudi Arabia:The upcoming visit of Saudi Arabia's crown prince Mohammed bin Salman (MBS) to India is not a routine affair."</li><li>"'s crown prince Mohammed bin Salman (MBS):The upcoming visit of Saudi Arabia's crown prince Mohammed bin Salman (MBS) to India is not a routine affair."</li><li>'to burnish his legitimacy after the international:The trip to India is evidently timed to burnish his legitimacy after the international opprobrium that followed the murder of The Washington Post journalist Jamal Khashoggi.'</li></ul>                                                                                                                                                      |
| Negative    | <ul><li>'that followed the murder of The Washington:The trip to India is evidently timed to burnish his legitimacy after the international opprobrium that followed the murder of The Washington Post journalist Jamal Khashoggi.'</li><li>"Arabia's disastrous military intervention in Yemen or:India for its part has refrained from even a hint of disapproval of Saudi Arabia's disastrous military intervention in Yemen or its misguided attempts to isolate Qatar, never mind the brutal assassination of Khashoggi."</li><li>'condemn the Soviet invasion but privately urged:India sought to adopt a more nuanced stance; it did not openly condemn the Soviet invasion but privately urged Moscow to pull back.'</li></ul> |
| Positive    | <ul><li>"in fostering stronger relations with countries in:Prime Minister Narendra Modi's government has invested considerable time and energy in fostering stronger relations with countries in West Asia."</li><li>"has invested considerable time and energy in fostering stronger:Prime Minister Narendra Modi's government has invested considerable time and energy in fostering stronger relations with countries in West Asia."</li><li>"security and economic ties with Saudi Arabia:Modi's visit to Riyadh in 2016 gave a fillip to security and economic ties with Saudi Arabia."</li></ul>                                                                                                                                |
| Ambivalent  | <ul><li>"a hint of disapproval of Saudi Arabia:India for its part has refrained from even a hint of disapproval of Saudi Arabia's disastrous military intervention in Yemen or its misguided attempts to isolate Qatar, never mind the brutal assassination of Khashoggi."</li></ul>                                                                                                                                                                                                                                                                                                                                                                                                                                                  |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.8387   |

## 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(
    "asadnaqvi/setfitabsa-aspect",
    "asadnaqvi/setfitabsa-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   | 11  | 27.7071 | 45  |

| Label       | Training Sample Count |
|:------------|:----------------------|
| Ambivalent  | 1                     |
| Informative | 73                    |
| Negative    | 20                    |
| Positive    | 5                     |

### 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
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True

### Training Results
| Epoch      | Step   | Training Loss | Validation Loss |
|:----------:|:------:|:-------------:|:---------------:|
| 0.0217     | 1      | 0.2604        | -               |
| **1.0870** | **50** | **0.0642**    | **0.3479**      |
| 2.1739     | 100    | 0.0249        | 0.3974          |
| 3.2609     | 150    | 0.0146        | 0.4341          |
| 4.3478     | 200    | 0.0033        | 0.4358          |

* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- spaCy: 3.7.4
- Transformers: 4.40.1
- PyTorch: 2.2.1+cu121
- Datasets: 2.19.0
- 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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