Model: pszemraj/t5-v1_1-base-ft-jflAUG
Author: pszemraj
Library: transformers
Tags: transformers, pytorch, safetensors, t5, text2text-generation, grammar, spelling, punctuation, error-correction, dataset:jfleg, license:cc-by-nc-sa-4.0, text-generation-inference, endpoints_compatible, region:us
Card metadata: license: cc-by-nc-sa-4.0
tags: grammar, spelling, punctuation, error-correction
datasets: jfleg
widget: text: i can has cheezburger
example_title: cheezburger, text: There car broke down so their hitching a ride to they're class.
example_title: compound-1, text: so em if we have an now so with fito ringina know how to estimate the tren given the ereafte mylite trend we can also em an estimate is nod s i again tort watfettering an we have estimated the trend an called wot to be called sthat of exty right now we can and look at wy this should not hare a trend i becan we just remove the trend an and we can we now estimate tesees ona effect of them exty
example_title: Transcribed Audio Example 2, text: My coworker said he used a financial planner to help choose his stocks so he wouldn't loose money.
example_title: incorrect word choice (context), text: good so hve on an tadley i'm not able to make it to the exla session on monday this week e which is why i am e recording pre recording an this excelleision and so to day i want e to talk about two things and first of all em i wont em wene give a summary er about ta ohow to remove trents in these nalitives from time series
example_title: lowercased audio transcription output, text: Frustrated, the chairs took me forever to set up.
example_title: dangling modifier, text: I would like a peice of pie.
example_title: miss-spelling, text: Which part of Zurich was you going to go hiking in when we were there for the first time together? ! ?
example_title: chatbot on Zurich
parameters: max_length: 128
min_length: 4
num_beams: 4
repetition_penalty: 1.21
length_penalty: 1
early_stopping: yes
Likes: 1 | All-time downloads: 2364
Created: 2022-04-02 18:05:54