Update README.md

README.md

@@ -1,34 +1,33 @@
----
-license: apache-2.0
----
-<h2>Re-Punctuate:</h2>
-
-Re-Punctuate is a T5 model that attempts to correct Capitalization and Punctuations in the sentences.
-
-<h3>DataSet:</h3>
-
-DialogSum dataset (115056 Records) was used to fine-tune the model for Punctuation and Capitalization correction.
-
-<h3>Usage:</h3>
-
-<pre>
-
-from transformers import T5Tokenizer, TFT5ForConditionalGeneration
-
-tokenizer = T5Tokenizer.from_pretrained('SJ-Ray/Re-Punctuate')
-model = TFT5ForConditionalGeneration.from_pretrained('SJ-Ray/Re-Punctuate')
-
-input_text = 'the story of this brave brilliant athlete whose very being was questioned so publicly is one that still captures the imagination'
-inputs = tokenizer.encode("punctuate: " + input_text, return_tensors="tf") 
-result = model.generate(inputs)
-
-decoded_output = tokenizer.decode(result[0], skip_special_tokens=True)
-print(decoded_output)
-
-</pre>
-<h4> Example: </h4>
-<b>Input:</b>  the story of this brave brilliant athlete whose very being was questioned so publicly is one that still captures the imagination <br>
-<b>Output:</b> The story of this brave, brilliant athlete, whose very being was questioned so publicly, is one that still captures the imagination.
-
-<h4> Connect on: </h4>
-LinkedIn : www.linkedin.com/in/suraj-kumar-710382a7
+---
+license: apache-2.0
+---
+<h2>Re-Punctuate:</h2>
+
+Re-Punctuate is a T5 model that attempts to correct Capitalization and Punctuations in the sentences.
+
+<h3>DataSet:</h3>
+
+DialogSum dataset (115056 Records) was used to fine-tune the model for Punctuation and Capitalization correction.
+
+<h3>Usage:</h3>
+
+<pre>
+
+from transformers import T5Tokenizer, TFT5ForConditionalGeneration
+
+tokenizer = T5Tokenizer.from_pretrained('SJ-Ray/Re-Punctuate')
+model = TFT5ForConditionalGeneration.from_pretrained('SJ-Ray/Re-Punctuate')
+
+input_text = 'the story of this brave brilliant athlete whose very being was questioned so publicly is one that still captures the imagination'
+inputs = tokenizer.encode("punctuate: " + input_text, return_tensors="tf") 
+result = model.generate(inputs)
+
+decoded_output = tokenizer.decode(result[0], skip_special_tokens=True)
+print(decoded_output)
+
+</pre>
+<h4> Example: </h4>
+<b>Input:</b>  the story of this brave brilliant athlete whose very being was questioned so publicly is one that still captures the imagination <br>
+<b>Output:</b> The story of this brave, brilliant athlete, whose very being was questioned so publicly, is one that still captures the imagination.
+
+<h4> Connect on: <a href="www.linkedin.com/in/suraj-kumar-710382a7">LinkedIn</a></h4>