Add usage example

README.md

@@ -31,6 +31,72 @@ SwissBERT contains the following language adapters:
 ## License
 Attribution-NonCommercial 4.0 International (CC BY-NC 4.0).
 
+## Usage (masked language modeling)
+
+```python
+from transformers import pipeline
+
+fill_mask = pipeline(model="ZurichNLP/swissbert")
+```
+
+### German example
+```python
+fill_mask.model.set_default_language("de_CH")
+fill_mask("Der schönste Kanton der Schweiz ist <mask>.")
+```
+Output:
+```
+[{'score': 0.1373230218887329,
+  'token': 331,
+  'token_str': 'Zürich',
+  'sequence': 'Der schönste Kanton der Schweiz ist Zürich.'},
+ {'score': 0.08464793860912323,
+  'token': 5903,
+  'token_str': 'Appenzell',
+  'sequence': 'Der schönste Kanton der Schweiz ist Appenzell.'},
+ {'score': 0.08250337839126587,
+  'token': 10800,
+  'token_str': 'Graubünden',
+  'sequence': 'Der schönste Kanton der Schweiz ist Graubünden.'},
+ {'score': 0.07495423406362534,
+  'token': 4833,
+  'token_str': 'Schwyz',
+  'sequence': 'Der schönste Kanton der Schweiz ist Schwyz.'},
+ {'score': 0.07253701984882355,
+  'token': 3734,
+  'token_str': 'Uri',
+  'sequence': 'Der schönste Kanton der Schweiz ist Uri.'}]
+```
+
+### French example
+```python
+fill_mask.model.set_default_language("fr_CH")
+fill_mask("Je m'appelle <mask> Federer.")
+```
+Output:
+```
+[{'score': 0.9943694472312927,
+  'token': 1371,
+  'token_str': 'Roger',
+  'sequence': "Je m'appelle Roger Federer."},
+ {'score': 0.00029945766436867416,
+  'token': 689,
+  'token_str': 'donc',
+  'sequence': "Je m'appelle donc Federer."},
+ {'score': 0.00022272868955042213,
+  'token': 71,
+  'token_str': 'r',
+  'sequence': "Je m'appeller Federer."},
+ {'score': 0.00020624867465812713,
+  'token': 10739,
+  'token_str': 'Robin',
+  'sequence': "Je m'appelle Robin Federer."},
+ {'score': 0.00016592108295299113,
+  'token': 15523,
+  'token_str': 'Bâlois',
+  'sequence': "Je m'appelle Bâlois Federer."}]
+```
+
 ## Bias, Risks, and Limitations
 - SwissBERT is mainly intended for tagging tokens in written text (e.g., named entity recognition, part-of-speech tagging), text classification, and the encoding of words, sentences or documents into fixed-size embeddings.
 SwissBERT is not designed for generating text.