updated read me

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README.md

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 # flexudy-pipe-question-generation-v2
 After transcribing your audio with Wav2Vec2, you might be interested in a post processor.
 
-I trained it with only 42K paragraphs from the SQUAD dataset. All paragraphs had at most 128 tokens (separated by white spaces)
+All paragraphs had at most 128 tokens (separated by white spaces)
 
 ```python
 from transformers import T5Tokenizer, T5ForConditionalGeneration
@@ -38,7 +38,7 @@ BEFORE HE HAD TIME TO ANSWER A MUCH ENCUMBERED VERA BURST INTO THE ROOM WITH THE
 ```
 OUTPUT 1:
 ```
-Before he had time to answer a much-enumbered era burst into the room with the question, I say, "Can I leave these here?" In 2002, these were a small black pig and a dusty specimen of black red game cock.
+Before he had time to answer a much encumbered vara burst into the room with the question, I say, can I leave these here. In 2002, these were a small black pig and a lusty specimen of black red game cock.
 ```
 
 INPUT 2:
@@ -48,7 +48,7 @@ GOING ALONG SLUSHY COUNTRY ROADS AND SPEAKING TO DAMP AUDIENCES IN DRAUGHTY SCHO
 
 OUTPUT 2:
 ```
-Going along Slushy Country Roads and speaking to damp audiences in Droughty School rooms day after day for a fortnight, he'll have to put in an appearance at some place of worship on Sunday morning and he can come to us immediately afterwards.
+Going along Slushy Country Roads and speaking to damp audiences in Draughty School Rooms Day After day for a weekend, he'll have to put in an appearance at some place of worship on Sunday morning and he can come to us immediately afterwards.
 ```
 I strongly recommend improving the performance via further fine-tuning or by training more examples.
 - Possible Quick Rule based improvements: Align the transcribed version and the generated version. If the similarity of two words (case-insensitive) vary by more than some threshold based on some similarity metric (e.g. Levenshtein), then keep the transcribed word.