--- library_name: setfit tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer datasets: - hojzas/proj4-uniq_srt-lab2 metrics: - accuracy widget: - text: return sorted(list(dict.fromkeys(it).keys())) - text: " uniq_list = list(set(it))\n uniq_list.sort()\n return uniq_list" - text: "it=sorted(set(list(it)))\n return it" - text: " sequence_list = list(it)\n unique_list = list(set(sequence_list))\n\ \ sorted_list = sorted(unique_list, reverse=False)\n return sorted_list" - text: return list(dict.fromkeys(sorted(it))) pipeline_tag: text-classification inference: true co2_eq_emissions: emissions: 0.7119941051678922 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: Intel(R) Xeon(R) Silver 4314 CPU @ 2.40GHz ram_total_size: 251.49161911010742 hours_used: 0.003 hardware_used: 4 x NVIDIA RTX A5000 base_model: sentence-transformers/all-mpnet-base-v2 --- # SetFit with sentence-transformers/all-mpnet-base-v2 This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [hojzas/proj4-uniq_srt-lab2](https://huggingface.co/datasets/hojzas/proj4-uniq_srt-lab2) dataset that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-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/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - **Maximum Sequence Length:** 384 tokens - **Number of Classes:** 3 classes - **Training Dataset:** [hojzas/proj4-uniq_srt-lab2](https://huggingface.co/datasets/hojzas/proj4-uniq_srt-lab2) ### 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 | |:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 |