Model: lucienbaumgartner/mentalizing-class
Author: lucienbaumgartner
Pipeline: text-classification
Library: setfit
Tags: setfit, safetensors, mpnet, sentence-transformers, text-classification, generated_from_setfit_trainer, arxiv:2209.11055, base_model:sentence-transformers/all-mpnet-base-v2, base_model:finetune:sentence-transformers/all-mpnet-base-v2, model-index, region:us
Base models (finetune): id: sentence-transformers/all-mpnet-base-v2
Card metadata: base_model: sentence-transformers/all-mpnet-base-v2
library_name: setfit
metrics: accuracy, precision, recall, f1
pipeline_tag: text-classification
tags: setfit, sentence-transformers, text-classification, generated_from_setfit_trainer
widget: chatgpt , in response to this , will propose an appropriate emoticon response for that comment as well generate the specific text of a comment reply for me to give ., text - to - speech : once chatgpt has generated a response , you will need to convert it into speech using a text - to - speech api , such as google text - to - speech or microsoft azure text - to - speech . , yes , i know that chatgpt said this many many times , but this is a fact that people need to eat it up and move on . , explanation : this makes it easier for chatgpt to understand and generate a response for each individual point . , chatgpt makes choices , algorithms are n't neutral . 
inference: yes
model-index: name: SetFit with sentence-transformers/all-mpnet-base-v2
results: task: type: text-classification
name: Text Classification
dataset: name: Unknown
type: unknown
split: test
metrics: type: accuracy
value: 0.75
name: Accuracy
verified: no, type: precision
value: 0.7666666666666666
name: Precision
verified: no, type: recall
value: 0.746031746031746
name: Recall
verified: no, type: f1
value: 0.7488344988344989
name: F1
verified: no
Likes: 0 | All-time downloads: 10
Created: 2024-10-16 14:11:26