metadata
license: apache-2.0
tags:
- generated_from_trainer
- email generation
- email
datasets:
- aeslc
- postbot/multi-emails-100k
widget:
- text: Thursday pay invoice need asap thanks Pierre good morning dear Harold
example_title: invoice
- text: dear elia when will space be ready need urgently regards ronald
example_title: space ready
- text: >-
Tuesday need talk with you important stuff dear jonathan status war in
Syria
example_title: war status
- text: dear bob will back wednesday need urgently regards elena
example_title: return wednesday
- text: dear mary thanks for your last invoice need know when payment be
example_title: last invoice
- text: >-
pct1_dropremainder rounding may truncate the last examples in a dataset if
the number of examples in your dataset don’t divide evenly by 100 dear bob
example_title: pct1_dropremainder
- text: >-
dear joseph have all invoices ready Monday next invoice in 30 days have
great weekend
example_title: next invoice
- text: >-
dear mary I have couple questions on new contract we agreed on need know
thoughts regarding contract
example_title: contract
- text: Friday will make report due soon please thanks dear john
example_title: report due soon
- text: >-
need take photos sunday want finish thursday photo exhibition need urgent
help thanks dear john
example_title: photo exhibition
- text: Tuesday need talk with you important stuff dear reginald
example_title: important talk
- text: dear maria how are you doing thanks very much
example_title: thanks
- text: >-
dear james tomorrow will prepare file for june report before leave need
know when leave
example_title: file for june report
parameters:
min_length: 32
max_length: 256
no_repeat_ngram_size: 2
do_sample: false
num_beams: 8
early_stopping: true
repetition_penalty: 5.5
length_penalty: 1.1
flan-t5-base-kw2email-v1
- keyword-to-email generation
- starting checkpoint was
google/flan-t5-base, trained for 5 epochs
TODO: more words