---
base_model: sentence-transformers/paraphrase-mpnet-base-v2
datasets:
- omymble/setfit-books-categories
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: His fantasy works are not cliché or based on traditional fantasy but they
    are full of fresh, imagination and worlds and characters we can learn to love
- text: I found this a good book from a good author
- text: This is dark fantasy at its best
- text: Mister Monday is an interesting Fantasy novel that draws readers in from the
    very beginning
- text: I found this a good book from a good author
inference: false
---

# SetFit Polarity Model with sentence-transformers/paraphrase-mpnet-base-v2

This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [omymble/setfit-books-categories](https://huggingface.co/datasets/omymble/setfit-books-categories) dataset that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-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 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:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-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:** [setfit-absa-aspect](https://huggingface.co/setfit-absa-aspect)
- **SetFitABSA Polarity Model:** [omymble/books-categories](https://huggingface.co/omymble/books-categories)
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 6 classes
- **Training Dataset:** [omymble/setfit-books-categories](https://huggingface.co/datasets/omymble/setfit-books-categories)
<!-- - **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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
|:-------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| BOOK#AUDIENCE      | <ul><li>'I recommend this for fans of fantasy, or other books by Garth Nix'</li><li>'I first got this book when I was eight and I totally loved it! I have read it every year since then! It is about a pair of twins who are born one VERY good and one EXTREMELY bad'</li><li>'However, I did feel one particular scene might be rather nightmare-inducing for the youngest readers - so recommend this for the ages of 12 and above'</li></ul>                                                                                                                                                      |
| BOOK#AUTHOR        | <ul><li>'Banks has writin better books than this book,'</li><li>"Now in is astonishing new novel, Michael Dobbs throws brilliant fresh light upon Churchill's relationship with the Soviet spy and the twenty months of conspiracy, chance and outright treachery that were to propel Churchill from outcast to messiah and change the course of history"</li><li>'Paul focuses on the problems of an intimate relationship and the decisions the teens make at that moment'</li></ul>                                                                                                                 |
| BOOK#GENERAL       | <ul><li>'This is the first book in the Keys to the Kingdom series by Garth Nix'</li><li>'The book is a great read right until the end, so rare in non-fiction'</li><li>'Anne Kingston did a marvellous job on this book'</li></ul>                                                                                                                                                                                                                                                                                                                                                                     |
| BOOK#TITLE         | <ul><li>'Personal I loved My Darling My Hamburger'</li><li>'But THE INTRUDERS is pretty much a middling effort, at least when it comes to the plot'</li><li>'After reading several pages I relented and purchased Mister Monday'</li></ul>                                                                                                                                                                                                                                                                                                                                                             |
| CONTENT#CHARACTERS | <ul><li>"She's not a great writer but she's a fabulous storyteller and her Tony Hill/Carol Jordan mysteries are the best of the bunch"</li><li>'but before he can do that he has to dodge fechters, run from enemys like Noon and Dawn, run from dinosaurs, try not to get killed, and try to prevent himself from having a asthma atackk!! But, thankfully he has some help from a girl named suzy, a guy named Dusk, and a talking toad'</li><li>'But when a fight emerges between the two figures - Mister Monday and Sneezer - they both disappear without any further regard to Arthur'</li></ul> |
| CONTENT#GENRE      | <ul><li>'I love fantasy and science fiction, but this storyteller forgot something very important'</li><li>'At first I was amused an entertained by Angela and Diabola the novel by Lynne Reid Banks, but as it progressed and became exceedingly darker, I read the jacket to find that this book was recommended for ages 9-12'</li><li>"Here's a thriller that really thrills"</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(
    "setfit-absa-aspect",
    "omymble/books-categories",
)
# 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   | 2   | 21.0917 | 78  |

| Label              | Training Sample Count |
|:-------------------|:----------------------|
| BOOK#AUDIENCE      | 20                    |
| BOOK#AUTHOR        | 20                    |
| BOOK#GENERAL       | 20                    |
| BOOK#TITLE         | 20                    |
| CONTENT#CHARACTERS | 20                    |
| CONTENT#GENRE      | 20                    |

### 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 |
|:----------:|:-------:|:-------------:|:---------------:|
| 0.0106     | 1       | 0.2623        | -               |
| 0.5319     | 50      | 0.1293        | -               |
| 1.0638     | 100     | 0.0132        | -               |
| 1.5957     | 150     | 0.0022        | -               |
| 2.1277     | 200     | 0.0027        | -               |
| 2.6596     | 250     | 0.0013        | -               |
| **3.1915** | **300** | **0.0017**    | **-**           |
| 3.7234     | 350     | 0.0015        | -               |
| 4.2553     | 400     | 0.0029        | -               |
| 4.7872     | 450     | 0.0015        | -               |
| 0.0106     | 1       | 0.0115        | -               |
| 0.5319     | 50      | 0.009         | 0.1324          |
| 1.0638     | 100     | 0.0094        | 0.1267          |
| 1.5957     | 150     | 0.0007        | 0.1194          |
| 2.1277     | 200     | 0.0017        | 0.1256          |
| 2.6596     | 250     | 0.0008        | 0.1293          |
| **3.1915** | **300** | **0.0007**    | **0.1173**      |
| 3.7234     | 350     | 0.0008        | 0.1231          |
| 4.2553     | 400     | 0.0023        | 0.1272          |
| 4.7872     | 450     | 0.0008        | 0.1241          |

* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.1.0
- 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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