Librarian Bot: Add base_model information to model

README.md

@@ -1,4 +1,6 @@
 ---
+language:
+- en
 license:
 - cc-by-nc-sa-4.0
 - apache-2.0
@@ -15,50 +17,40 @@ widget:
   example_title: compound-1
 - text: i can has cheezburger
   example_title: cheezburger
-- 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
+- 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.
+- 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
+- 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
+- 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
-- text: >-
-    Most of the course is about semantic or  content of language but there are
-    also interesting topics to be learned from the servicefeatures except
-    statistics in characters in documents. At this point, Elvthos introduces
-    himself as his native English speaker and goes on to say that if you
-    continue to work on social scnce,
+- text: Most of the course is about semantic or  content of language but there are
+    also interesting topics to be learned from the servicefeatures except statistics
+    in characters in documents. At this point, Elvthos introduces himself as his native
+    English speaker and goes on to say that if you continue to work on social scnce,
   example_title: social science ASR summary output
-- text: >-
-    they are somewhat nearby right yes please i'm not sure how the innish is
-    tepen thut mayyouselect one that istatte lo variants in their property e ere
-    interested and anyone basical e may be applyind reaching the browing
-    approach were
+- text: they are somewhat nearby right yes please i'm not sure how the innish is tepen
+    thut mayyouselect one that istatte lo variants in their property e ere interested
+    and anyone basical e may be applyind reaching the browing approach were
 - medical course audio transcription
-inference: False
+inference: false
 pipeline_tag: text2text-generation
-language:
-- en
+base_model: facebook/bart-base
 ---