Update README.md

README.md

@@ -1,30 +1,31 @@
 ---
 title: METEOR
-emoji: 🤗
+emoji: 🤗 
 colorFrom: blue
 colorTo: red
 sdk: gradio
-sdk_version: 5.29.0
+sdk_version: 3.19.1
 app_file: app.py
 pinned: false
 tags:
 - evaluate
 - metric
 description: >-
-  METEOR, an automatic metric for machine translation evaluation that is based
-  on a generalized concept of unigram matching between the machine-produced
-  translation and human-produced reference translations. Unigrams can be matched
-  based on their surface forms, stemmed forms, and meanings; furthermore, METEOR
-  can be easily extended to include more advanced matching strategies. Once all
-  generalized unigram matches between the two strings have been found, METEOR
-  computes a score for this matching using a combination of unigram-precision,
-  unigram-recall, and a measure of fragmentation that is designed to directly
-  capture how well-ordered the matched words in the machine translation are in
-  relation to the reference.
-
-  METEOR gets an R correlation value of 0.347 with human evaluation on the
-  Arabic data and 0.331 on the Chinese data. This is shown to be an improvement
-  on using simply unigram-precision, unigram-recall and their harmonic F1
+  METEOR, an automatic metric for machine translation evaluation
+  that is based on a generalized concept of unigram matching between the
+  machine-produced translation and human-produced reference translations.
+  Unigrams can be matched based on their surface forms, stemmed forms,
+  and meanings; furthermore, METEOR can be easily extended to include more
+  advanced matching strategies. Once all generalized unigram matches
+  between the two strings have been found, METEOR computes a score for
+  this matching using a combination of unigram-precision, unigram-recall, and
+  a measure of fragmentation that is designed to directly capture how
+  well-ordered the matched words in the machine translation are in relation
+  to the reference.
+  
+  METEOR gets an R correlation value of 0.347 with human evaluation on the Arabic
+  data and 0.331 on the Chinese data. This is shown to be an improvement on
+  using simply unigram-precision, unigram-recall and their harmonic F1
   combination.
 ---
 
@@ -134,4 +135,5 @@ Furthermore, while the alignment and matching done in METEOR is based on unigram
     
 ## Further References 
 - [METEOR -- Wikipedia](https://en.wikipedia.org/wiki/METEOR)
-- [METEOR score -- NLTK](https://www.nltk.org/_modules/nltk/translate/meteor_score.html)
+- [METEOR score -- NLTK](https://www.nltk.org/_modules/nltk/translate/meteor_score.html)
+