Model: HelgeKn/SemEval-multi-class-4
Author: HelgeKn
Pipeline: text-classification
Library: setfit
Tags: setfit, safetensors, mpnet, sentence-transformers, text-classification, generated_from_setfit_trainer, arxiv:2209.11055, base_model:sentence-transformers/paraphrase-mpnet-base-v2, base_model:finetune:sentence-transformers/paraphrase-mpnet-base-v2, model-index, region:us
Base models (finetune): id: sentence-transformers/paraphrase-mpnet-base-v2
Card metadata: library_name: setfit
tags: setfit, sentence-transformers, text-classification, generated_from_setfit_trainer
metrics: accuracy
widget: The Alavas worked themselves to the bone in the last period , and English and San Emeterio ( 65-75 ) had already made it clear that they were not going to let anyone take away what they had earned during the first thirty minutes . , To break the uncomfortable silence , Haney began to talk . , For the treatment of non-small cell lung cancer , the effects of Alimta were compared with those of docetaxel ( another anticancer medicine ) in one study involving 571 patients with locally advanced or metastatic disease who had received chemotherapy in the past . , As we all know , a few minutes before the end of the game ( that their team had already won ) , both players deliberately wasted time which made the referee show the second yellow card to both of them . , In contrast , patients whose cancer was affecting squamous cells had shorter survival times if they received Alimta . 
pipeline_tag: text-classification
inference: yes
base_model: sentence-transformers/paraphrase-mpnet-base-v2
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: 0.14172185430463577
name: Accuracy
verified: no
Likes: 0 | All-time downloads: 15
Created: 2023-12-14 13:05:58