---
base_model: BAAI/bge-small-en-v1.5
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Mister Monday and Sneezer - they both:But when a fight emerges between the
    two figures - Mister Monday and Sneezer - they both disappear without any further
    regard to Arthur
- text: the cat or animal lover:Great for the cat or animal lover
- text: a truly likable character:THE INTRUDERS is further weakened by the lack of
    a truly likable character
- text: '''s novel "keys of the Kingdom Mister Monday" is a:The children''s novel
    "keys of the Kingdom Mister Monday" is a hardcore mix beetween mystery and science
    fiction'
- text: If books on criminal profiling and psychological forensics:If books on criminal
    profiling and psychological forensics are your thing, you'll probably really enjoy
    McDermid's work
inference: false
---

# 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:** [omymble/books-full-bge-aspect](https://huggingface.co/omymble/books-full-bge-aspect)
- **SetFitABSA Polarity Model:** [omymble/books-full-bge-polarity](https://huggingface.co/omymble/books-full-bge-polarity)
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 3 classes
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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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
|:---------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| negative | <ul><li>"too dark for younger ones, unless you:It might be an entertaining point of discussion with a child 12 or older, but it's too dark for younger ones, unless you're ready to talk about true evil, adult motivations, supernatural forces, and fratricide!"</li><li>'The mystery is secondary to:The mystery is secondary to the rest of the story and is only really approached in the remaining 30 pages of the book'</li><li>'was only my book with this problem:I have no idea if it was only my book with this problem'</li></ul>                                                            |
| neutral  | <ul><li>'world, as Nix weaves a wonderful:-enjoy the genre of fantasies, of a unknown world, as Nix weaves a wonderful tale of the things that will open your eyes to a different world'</li><li>'Arthur must get through:Arthur must get through some horrifying trials to save his Earth from the plague, and to prove that he is the Rightful Heir'</li><li>'to say that Mister Monday is definitely worth:I was interested enough in the strange and original concept to read on to the next book, so I would venture to say that Mister Monday is definitely worth reading at least once'</li></ul> |
| positive | <ul><li>'I recommend THE INTRUDERS if you enjoy:I recommend THE INTRUDERS if you enjoy good writing, but if you want a great story, you should try THE STRAW MEN instead'</li><li>'of the major bios on "Big:I\'ve read all of the major bios on "Big Al" and this is by far the best'</li><li>'really great fantasy book:this is a really great fantasy book'</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(
    "omymble/books-full-bge-aspect",
    "omymble/books-full-bge-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   | 3   | 25.1976 | 78  |

| Label    | Training Sample Count |
|:---------|:----------------------|
| negative | 14                    |
| neutral  | 91                    |
| positive | 62                    |

### 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: 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 |
|:----------:|:--------:|:-------------:|:---------------:|
| 0.0041     | 1        | 0.2476        | -               |
| 0.2049     | 50       | 0.2339        | -               |
| 0.4098     | 100      | 0.2053        | -               |
| 0.6148     | 150      | 0.0231        | -               |
| 0.8197     | 200      | 0.0038        | -               |
| 1.0246     | 250      | 0.0018        | -               |
| 1.2295     | 300      | 0.0017        | -               |
| 1.4344     | 350      | 0.0014        | -               |
| 1.6393     | 400      | 0.0013        | -               |
| 1.8443     | 450      | 0.001         | -               |
| 2.0492     | 500      | 0.001         | -               |
| 2.2541     | 550      | 0.0007        | -               |
| 2.4590     | 600      | 0.0006        | -               |
| 2.6639     | 650      | 0.0007        | -               |
| 2.8689     | 700      | 0.0006        | -               |
| 3.0738     | 750      | 0.0008        | -               |
| 3.2787     | 800      | 0.0007        | -               |
| 3.4836     | 850      | 0.0007        | -               |
| 3.6885     | 900      | 0.0006        | -               |
| 3.8934     | 950      | 0.0006        | -               |
| **4.0984** | **1000** | **0.0007**    | **0.2748**      |
| 4.3033     | 1050     | 0.0009        | -               |
| 4.5082     | 1100     | 0.0006        | -               |
| 4.7131     | 1150     | 0.0006        | -               |
| 4.9180     | 1200     | 0.0005        | -               |

* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- spaCy: 3.7.4
- Transformers: 4.39.0
- PyTorch: 2.3.1+cu121
- Datasets: 2.20.0
- Tokenizers: 0.15.2

## 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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