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SmartIU2
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setfit-imdb-absa-action-v1.0-aspect

Text Classification
setfit
Safetensors
sentence-transformers
English
roberta
absa
generated_from_setfit_trainer
Model card Files
xet
Community
setfit-imdb-absa-action-v1.0-aspect
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---
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget: []
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: false
language:
- en
base_model:
- sentence-transformers/all-distilroberta-v1
---

## Usage

This model was created with a [Setfit Fork](https://github.com/smartIU2/setfit) using a custom aspect extractor.

It is intended to be used in conjunction with [IMDb_ABSA](https://github.com/smartIU2/imdb_absa) only.


Default setfit model card below:


# SetFit Aspect Model

A [SetFit](https://github.com/huggingface/setfit) model can be used for Aspect Based Sentiment Analysis (ABSA). 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:** sentence-transformers/all-distilroberta-v1
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **spaCy Model:** en_core_web_trf
- **SetFitABSA Aspect Model:** [SmartIU2/setfit-imdb-absa-action-v1.0-aspect](https://huggingface.co/SmartIU2/setfit-imdb-absa-action-v1.0-aspect)
- **SetFitABSA Polarity Model:** [SmartIU2/setfit-imdb-absa-action-v1.0-polarity](https://huggingface.co/SmartIU2/setfit-imdb-absa-action-v1.0-polarity)
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 2 classes
- **Language:** English

### Original Setfit 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)

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## Training Details

### Framework Versions
- Python: 3.10.6
- SetFit: 1.1.2
- Sentence Transformers: 4.1.0
- spaCy: 3.7.5
- Transformers: 4.52.4
- PyTorch: 2.7.0+cu128
- Datasets: 3.6.0
- Tokenizers: 0.21.1

## 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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