Update README.md

README.md

@@ -1,31 +1,30 @@
 ---
 title: METEOR
-emoji: 🤗 
+emoji: 🤗
 colorFrom: blue
 colorTo: red
 sdk: gradio
-sdk_version: 3.19.1
+sdk_version: 5.29.0
 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.
 ---
 
@@ -135,5 +134,4 @@ 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)