README.md

metadata

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 using a custom aspect extractor.

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

Default setfit model card below:

SetFit Polarity 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 classifying aspect polarities.

The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a Sentence Transformer 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: sentence-transformers/all-distilroberta-v1
• Classification head: a LogisticRegression instance
• spaCy Model: en_core_web_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: 5 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

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}
}