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Librarian Bot: Add base_model information to model (#2)
Browse files- Librarian Bot: Add base_model information to model (97cabd5e242da7d395a41f72771d2fa4f839f05b)
Co-authored-by: Librarian Bot (Bot) <[email protected]>
README.md
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---
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- en
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license:
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- cc-by-nc-sa-4.0
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- apache-2.0
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tags:
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- error-correction
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- grammar synthesis
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- FLAN
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datasets:
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- jfleg
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widget:
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example_title:
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- text:
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example_title:
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- text:
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inference:
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parameters:
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max_length: 96
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num_beams: 2
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repetition_penalty: 1.15
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length_penalty: 1
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early_stopping:
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---
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# grammar-synthesis: flan-t5-xl
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---
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license:
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- cc-by-nc-sa-4.0
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- apache-2.0
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tags:
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- error-correction
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- grammar synthesis
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- FLAN
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datasets:
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- jfleg
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languages:
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- en
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widget:
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- text: There car broke down so their hitching a ride to they're class.
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example_title: compound-1
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- text: i can has cheezburger
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example_title: cheezburger
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- text: so em if we have an now so with fito ringina know how to estimate the tren
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given the ereafte mylite trend we can also em an estimate is nod s i again tort
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watfettering an we have estimated the trend an called wot to be called sthat of
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exty right now we can and look at wy this should not hare a trend i becan we just
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remove the trend an and we can we now estimate tesees ona effect of them exty
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example_title: Transcribed Audio Example 2
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- text: My coworker said he used a financial planner to help choose his stocks so
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he wouldn't loose money.
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example_title: incorrect word choice (context)
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- text: good so hve on an tadley i'm not able to make it to the exla session on monday
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this week e which is why i am e recording pre recording an this excelleision and
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so to day i want e to talk about two things and first of all em i wont em wene
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give a summary er about ta ohow to remove trents in these nalitives from time
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series
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example_title: lowercased audio transcription output
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- text: Frustrated, the chairs took me forever to set up.
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example_title: dangling modifier
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- text: I would like a peice of pie.
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example_title: simple miss-spelling
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- text: Which part of Zurich was you going to go hiking in when we were there for
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the first time together? ! ?
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example_title: chatbot on Zurich
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- text: Most of the course is about semantic or content of language but there are
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also interesting topics to be learned from the servicefeatures except statistics
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in characters in documents. At this point, Elvthos introduces himself as his native
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English speaker and goes on to say that if you continue to work on social scnce,
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example_title: social science ASR summary output
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- text: they are somewhat nearby right yes please i'm not sure how the innish is tepen
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thut mayyouselect one that istatte lo variants in their property e ere interested
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and anyone basical e may be applyind reaching the browing approach were
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- example_title: medical course audio transcription
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inference:
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parameters:
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max_length: 96
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num_beams: 2
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repetition_penalty: 1.15
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length_penalty: 1
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early_stopping: true
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base_model: google/flan-t5-xl
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---
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# grammar-synthesis: flan-t5-xl
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