--- base_model: sentence-transformers/paraphrase-mpnet-base-v2 library_name: setfit metrics: - accuracy pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: 'Colloqujdi Gio: Lodovico Vives latini, e volgari/Colloqui' - text: Ioannis Lodovici Vivis Von Underweÿsung ayner christlichen Frauwen drey Bücher ...erklärt unnd verteütscht. Durch Christophorum Brunonem .../Von Underweysung ayner christlichen Frauwen drey Bücher - text: Absolvtissimae in Hebraicam lingvam institvtiones accvratissime in vsvm studiosæ juuentutis conscriptæ ...Avtore Iohanne Isaaco Leuita Germano/Absolutissimae in Hebraicam linguam institutiones accuratissime in usum studiosæ juventutis conscriptæ ... Autore Iohanne Isaaco Levita Germano - text: In tertiam partem D. Thomæ Aqvinatis commentaria Ioannis Wiggers ... a quæstione I. vsque ad quæstionem XXVI. de verbo incarnatoIn tertiam partem D. Thomae Aquinatis commentaria Ioannis Wiggers ... a quaestione I. usque ad quaestionem XXVI. de verbo incarnato - text: Tabvla in grammaticen Hebræam,authore Nicolao Clenardo. A Iohanne Quinquarboreo Aurilacensi à mendis quibus scatebat repurgata, & annotationibus illustrata./Tabula in grammaticen Hebraeam, authore Nicolao Clenardo. A Johanne Quinquarboreo Aurilacensi à mendis quibus scatebat repurgata, & annotationibus illustrata inference: true model-index: - name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2 results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: accuracy value: 0.735 name: Accuracy --- # SetFit with sentence-transformers/paraphrase-mpnet-base-v2 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. 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. 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. ## 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 - **Maximum Sequence Length:** 512 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 | |:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | no |