Update README.md

README.md

@@ -13,7 +13,8 @@ language:
 - de 
 - el 
 - es 
-- fr  
+- fr
+- hi
 - ru 
 - sw 
 - th 
@@ -53,11 +54,11 @@ sequence_to_classify = "Angela Merkel ist eine Politikerin in Deutschland und Vo
 candidate_labels = ["politics", "economy", "entertainment", "environment"]
 classifier(sequence_to_classify, candidate_labels)
 #{'sequence': 'Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU',
-# 'labels': ['politics', 'economy', 'entertainment', 'environment'],
-# 'scores': [0.6869393587112427,
-#  0.18112628161907196,
-#  0.07022464275360107,
-#  0.06170979142189026]}
+# 'labels': ['politics', 'economy', 'environment', 'entertainment'],
+# 'scores': [0.4970444142818451,
+#  0.3297286927700043,
+#  0.08716338872909546,
+#  0.086063452064991]}
 ```
 
 If more than one candidate label can be correct, pass `multi_class=True` to calculate each class independently:
@@ -66,20 +67,20 @@ If more than one candidate label can be correct, pass `multi_class=True` to calc
 candidate_labels = ["politics", "economy", "entertainment", "environment"]
 classifier(sequence_to_classify, candidate_labels, multi_label=True)
 #{'sequence': 'Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU',
-# 'labels': ['politics', 'economy', 'entertainment', 'environment'],
-# 'scores': [0.9068101644515991,
-#  0.2646591067314148,
-#  0.00299322628416121,
-#  0.0016029390972107649]}
+# 'labels': ['politics', 'economy', 'environment', 'entertainment'],
+# 'scores': [0.6669772267341614,
+#  0.4559520483016968,
+#  0.004513110034167767,
+#  0.0035143839195370674]}
 ```
 
 ### Eval results
-The model was evaluated using the XNLI test sets on 14 languages: English (en), Arabic (ar), Bulgarian (bg), German (de), Greek (el), Spanish (es), French (fr), Russian (ru), Swahili (sw), Thai (th), Turkish (tr), Urdu (ur), Vietnam (vi) and Chinese (zh). The metric used is accuracy.
+The model was evaluated using the XNLI test sets on 15 languages: English (en), Arabic (ar), Bulgarian (bg), German (de), Greek (el), Spanish (es), French (fr), Hindi (hi), Russian (ru), Swahili (sw), Thai (th), Turkish (tr), Urdu (ur), Vietnam (vi) and Chinese (zh). The metric used is accuracy.
 
-|Datasets|en|ar|bg|de|el|es|fr|ru|sw|th|tr|ur|vi|zh|
-| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
-|[multilingual-e5-base-xnli](https://huggingface.co/mjwong/multilingual-e5-base-xnli)|0.849|0.771|0.800|0.796|0.795|0.812|0.801|0.783|0.731|0.767|0.771|0.710|0.789|0.786|
-|[multilingual-e5-large-xnli](https://huggingface.co/mjwong/multilingual-e5-large-xnli)|0.867|0.798|0.829|0.821|0.820|0.838|0.828|0.810|0.752|0.787|0.794|0.726|0.804|0.810|
+|Datasets|en|ar|bg|de|el|es|fr|hi|ru|sw|th|tr|ur|vi|zh|
+| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
+|[multilingual-e5-base-xnli](https://huggingface.co/mjwong/multilingual-e5-base-xnli)|0.849|0.771|0.800|0.796|0.795|0.812|0.801|-|0.783|0.731|0.767|0.771|0.710|0.789|0.786|
+|[multilingual-e5-large-xnli](https://huggingface.co/mjwong/multilingual-e5-large-xnli)|0.867|0.791|0.832|0.825|0.823|0.837|0.824|0.778|0.806|0.749|0.787|0.793|0.738|0.813|0.808|
 
 ### Training hyperparameters