Update README.md

README.md

@@ -30,9 +30,7 @@ widget:
 
 In this repository, we present our german zeroshot model. 
 
-This model was trained on the basis of the German BERT large model from [deepset.ai](https://huggingface.co/deepset/gbert-large) and finetuned for natural language inference based on 847.862 machine-translated nli sentence pairs, using the [mnli](https://huggingface.co/datasets/multi_nli), [anli](https://huggingface.co/datasets/anli) and [snli](https://huggingface.co/datasets/snli) datasets.
-
-For this purpose, we translated the sentence pairs in these dataset to German.
+This model was trained on the basis of the German BERT large model from [deepset.ai](https://huggingface.co/deepset/gbert-large) and finetuned for natural language inference based on 847.862 machine-translated nli sentence pairs, using the [mnli](https://huggingface.co/datasets/multi_nli), [anli](https://huggingface.co/datasets/anli) and [snli](https://huggingface.co/datasets/snli) datasets. For this purpose, we translated the sentence pairs in these dataset to German.
 
 If you are a German speaker you may also have a look at our Blog post about Zeroshot Classification and our model. 
 
@@ -76,7 +74,11 @@ The model requires you to specify labels (ideally labels suited to your task),
 a sequence (or list of sequences) to classify and a hypothesis template. 
 In our tests, if the labels comprise only single words, 
 "In diesem Satz geht es um das Thema {}" performed the best. 
- 
+
+However, for multiple words, especially when they combine nouns and verbs,
+Simple hypothesis such as "Weil" or "Daher" may perform better. 
+
+Here is an example of how to use the model:  
 
 ```python
 
@@ -90,38 +92,12 @@ sequence = "Ich habe ein Problem mit meinem Iphone das so schnell wie möglich g
 
 labels = ["Computer", "Handy", "Tablet", "dringend", "nicht dringend"] 
 
-#hypothesis_template = "In diesem Satz geht es um das Thema {}."     ## Since monolingual model,its sensitive to hypothesis template. This can be experimented
-#hypothesis_template = "Dieser Satz drückt ein Gefühl von {} aus."
+hypothesis_template = "In diesem Satz geht es um das Thema {}."    
 
 zershot_pipeline(sequence, labels, hypothesis_template=hypothesis_template)
 
 ```
 
-## Other Applications
-
-
-
-DESCRIPTION GOES HERE: 
-Satz 1:
-"Ich habe ein Problem mit meinem Iphone das so schnell wie möglich gelöst werden muss" 
-Satz 2: 
-"Ich hab ein kleines Problem mit meinem Macbook, und auch wenn die Reparatur nicht eilt, würde ich es gerne addressieren."
-Label: 
-["Computer", "Handy", "Tablet", "dringend", "nicht dringend"] 
-
-EMOTION EXAMPLE:
-"Ich bin entäuscht, dass ich kein Ticket für das Konzert meiner Lieblingsband bekommen habe."
-label: "Furcht, Freude, Wut , Überraschung,  Traurigkeit, Ekel, Verachtung"
-
- 
-   - text: "Wer ist die reichste Person der Welt" 
-
-  candidate_labels: "Frage, Schlagwörter"
-
-  hypothesis_template: "Hierbei handelt es sich um {}."
-
-""""""""
-
 ### Contact
 - Daniel Ehnes, daniel.ehnes@sva.de
 - Baran Avinc, baran.avinc@sva.de