Add description to card metadata

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.

README.md

@@ -1,6 +1,6 @@
 ---
 title: METEOR
-emoji: 🤗 
+emoji: 🤗
 colorFrom: blue
 colorTo: red
 sdk: gradio
@@ -8,10 +8,41 @@ sdk_version: 3.0.2
 app_file: app.py
 pinned: false
 tags:
-- evaluate
-- metric
----
+  - 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
+
+  combination.
+---
 # Metric Card for METEOR
 
 ## Metric description