Update README.md

README.md

@@ -33,13 +33,15 @@ This model was trained on the basis of the German BERT large model from [deepset
 
 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. 
+
 ### Model Details
 
 | | Description or Link  |
 |---|---|
 |**Base model**   | [```gbert-large```](https://huggingface.co/deepset/gbert-large) |
 |**Finetuning task**| Text Pair Classification / Natural Language Inference  |
-|**Source dataset**| [```mnli```](https://huggingface.co/datasets/multi_nli) ; [```anli```](https://huggingface.co/datasets/anli) ; [```snli```](https://huggingface.co/datasets/snli)   |
+|**Source datasets**| [```mnli```](https://huggingface.co/datasets/multi_nli) ; [```anli```](https://huggingface.co/datasets/anli) ; [```snli```](https://huggingface.co/datasets/snli)   |
 
 ### Performance
 
@@ -63,6 +65,30 @@ The next table shows the results as well as a comparison with other German langu
 | deepset/gbert-base | 0.65 |
 
 
+## How to use 
+
+The simplest way to use the model is the hugging-face transformers pipeline tool. 
+Just initialize the pipeline specifying the task as "zero-shot-classification" 
+
+```python
+
+from transformers import pipeline
+
+zershot_pipeline = pipeline("zero-shot-classification",
+
+                      model="svalabs/gbert-large-zeroshot-nli")
+
+sequence = "Ich habe ein Problem mit meinem Iphone das so schnell wie möglich gelöst werden muss" 
+
+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."
+
+zershot_pipeline(sequence, labels, hypothesis_template=hypothesis_template)
+
+```
+
 ## Other Applications
 
 
@@ -88,23 +114,6 @@ label: "Furcht, Freude, Wut , Überraschung,  Traurigkeit, Ekel, Verachtung"
 
 """"""""
 
-
-
-```python
-
-from transformers import pipeline
-
-classifier = pipeline("zero-shot-classification",
-
-                      model="Dehnes/zeroshot_gbert")
-
-sequence = "Ich habe ein Problem mit meinem Iphone das so schnell wie möglich gelöst werden muss" 
-
-candidate_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."
-
-classifier(sequence, candidate_labels, hypothesis_template=hypothesis_template)
-
-```
+### Contact
+- Daniel Ehnes, daniel.ehnes@sva.de
+- Baran Avinc, baran.avinc@sva.de