update generation params

README.md

@@ -1,22 +1,23 @@
 ---
-license: apache-2.0
+license: cc-by-nc-sa-4.0
 tags:
 - grammar
 - spelling
 - punctuation
 - error-correction
-
+datasets:
+- jfleg
 widget:
-- text: "Anna and Mike is going skiing"
-  example_title: "skiing"
+- 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: "I would like a peice of pie."
-  example_title: "miss-spelling"
 - 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
@@ -25,26 +26,66 @@ 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: True
+---
+---
+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"
 
-inference:
-  parameters:
-    no_repeat_ngram_size: 4
-    max_length: 128
-    min_length: 4
-    num_beams: 4
-    repetition_penalty: 1.51
-    length_penalty: 1
-    early_stopping: True
+parameters:
+  max_length: 128
+  min_length: 2
+  num_beams: 4
+  repetition_penalty: 1.21
+  length_penalty: 1
+  early_stopping: True
 ---
 
+> A more recent version can be found [here](https://huggingface.co/pszemraj/grammar-synthesis-large). Training smaller and/or comparably sized models is a WIP.
 
 # t5-v1_1-base-ft-jflAUG
 
-> **GOAL:** a more robust and generalized grammar and spelling correction model that corrects everything in a single shot. It should have a minimal impact on the semantics of correct sentences (i.e. it does not change things that do not need to be changed).
+**GOAL:** a more robust and generalized grammar and spelling correction model that corrects everything in a single shot. It should have a minimal impact on the semantics of correct sentences (i.e. it does not change things that do not need to be changed).
 
 - this model _(at least from preliminary testing)_ can handle large amounts of errors in the source text (i.e. from audio transcription) and still produce cohesive results. 
 - a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) on an expanded version of the [JFLEG dataset](https://aclanthology.org/E17-2037/).