Update README.md

README.md

@@ -1,20 +1,78 @@
 ---
-license: apache-2.0
+languages:
+- en
+license:
+- cc-by-nc-sa-4.0
+- apache-2.0
 tags:
-- generated_from_trainer
+- grammar
+- spelling
+- punctuation
+- error-correction
+- grammar synthesis
 datasets:
-- pszemraj/jflAUG-v5
-model-index:
-- name: bart-base-jflAUG-v5-r1
-  results: []
+- jfleg
+widget:
+- text: There car broke down so their hitching a ride to they're class.
+  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
+  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
+- 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
+- medical course audio transcription
+parameters:
+  max_length: 128
+  min_length: 4
+  num_beams: 8
+  repetition_penalty: 1.21
+  length_penalty: 1
+  early_stopping: true
+language:
+- en
+pipeline_tag: text2text-generation
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
 
-# bart-base-jflAUG-v5-r1
+# bart-base-grammar-synthesis
 
-This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the pszemraj/jflAUG-v5 dataset.
+This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an expanded version of the JFLEG dataset.
 
 ## Model description
 
@@ -54,4 +112,4 @@ The following hyperparameters were used during training:
 - Transformers 4.28.1
 - Pytorch 2.0.1+cu117
 - Datasets 2.12.0
-- Tokenizers 0.13.3
+- Tokenizers 0.13.3