--- base_model: sentence-transformers/all-MiniLM-L6-v2 library_name: setfit metrics: - accuracy pipeline_tag: text-classification tags: - setfit - absa - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: world:Though Arthur skips to another world, he's clearly from our own - text: attire:Among those are the army of doglike and winged creatures, all dressed in attire befitting a civilization one hundred and fifty years ago - text: Mister Monday:This is a 361 page book about a boy named Arthur Penhaligon who is destined to die an early death, but is saved by a key given to him by a mysterious man named Mister Monday - text: parents:Do their parents understand or even care about them? Are they ready for sex? Meanwhile can Maggie and Dennis learn to communicate enough to stay together? - text: boy:This is a 361 page book about a boy named Arthur Penhaligon who is destined to die an early death, but is saved by a key given to him by a mysterious man named Mister Monday inference: false model-index: - name: SetFit Aspect Model with sentence-transformers/all-MiniLM-L6-v2 results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: accuracy value: 0.8541666666666666 name: Accuracy --- # SetFit Aspect Model with sentence-transformers/all-MiniLM-L6-v2 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co./sentence-transformers/all-MiniLM-L6-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 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 body:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co./sentence-transformers/all-MiniLM-L6-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:** [omymble/setfit-absa-books-aspect](https://huggingface.co./omymble/setfit-absa-books-aspect) - **SetFitABSA Polarity Model:** [omymble/setfit-absa-books-polarity](https://huggingface.co./omymble/setfit-absa-books-polarity) - **Maximum Sequence Length:** 256 tokens - **Number of Classes:** 2 classes ### 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 | |:----------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | aspect |