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

Usage

This model was created with a Setfit Fork using a custom aspect extractor.

It is intended to be used in conjunction with IMDb_ABSA only.

Default setfit model card below:

SetFit Aspect Model

A SetFit model can be used for Aspect Based Sentiment Analysis (ABSA). A LogisticRegression 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. 1.Fine-tuning a Sentence Transformer with contrastive learning.
2. 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. 1.Use a spaCy model to select possible aspect span candidates.
2. 2.Use this SetFit model to filter these possible aspect span candidates.
3. 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 instance
• —spaCy Model: encoreweb_trf
• —SetFitABSA Aspect Model: SmartIU2/setfit-imdb-absa-action-v1.0-aspect
• —SetFitABSA Polarity Model: 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
• —Paper: Efficient Few-Shot Learning Without Prompts
• —Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

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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

@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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