Model: quantisan/paraphrase-MiniLM-L3-v2-93dataset-v3labels
Author: quantisan
Pipeline: text-classification
Library: setfit
Tags: setfit, safetensors, bert, sentence-transformers, text-classification, generated_from_setfit_trainer, arxiv:2209.11055, base_model:sentence-transformers/paraphrase-MiniLM-L3-v2, base_model:finetune:sentence-transformers/paraphrase-MiniLM-L3-v2, model-index, region:us
Base models (finetune): id: sentence-transformers/paraphrase-MiniLM-L3-v2
Card metadata: base_model: sentence-transformers/paraphrase-MiniLM-L3-v2
library_name: setfit
metrics: accuracy
pipeline_tag: text-classification
tags: setfit, sentence-transformers, text-classification, generated_from_setfit_trainer
widget: metrics.statistics.syllables: 170648.1, 7686.9, 33810.3, 106802.1, 28752.3, 132642.9, 29187.0, 92593.8, 55913.4, 33722.1, 294744.6, 137215.8, 240762.6, 363292.2, 183016.8, 4864.5, 202932.9, 71705.7, 105003.9, 787942.8, company.sector:  Software,  Finance,  Communications, pharmaceuticals, technology, Fashion, real estate,  software, banking and insurance, groceries, construction/real estate/banking,   Oil refining,  Oil refining, retail,  retail,  casinos, food packaging, cars, cosmetics, None, variety: Western, Eastern, Data.Fiber: 0.0, 0.2, 0.3, 0.4, 0.7, 0.1, 1.0, 0.6, 0.5, 1.9, 1.1, 2.3, 0.8, 1.6, 0.9, 1.2, 37.0, 4.5, 9.1, 1.5, Date.Month: 8, 3, 4, 5, 6, 7, 9, 10, 11, 12, 1, 2
inference: yes
model-index: name: SetFit with sentence-transformers/paraphrase-MiniLM-L3-v2
results: task: type: text-classification
name: Text Classification
dataset: name: Unknown
type: unknown
split: test
metrics: type: accuracy
value: 0.7698072805139187
name: Accuracy
verified: no
Likes: 0 | All-time downloads: 11
Created: 2024-10-15 21:34:37