Update README.md

README.md

@@ -17,6 +17,36 @@ widget:
 - zero stage 0
 - tp/pp = 1
 
+
+### How to use
+
+You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we
+set a seed for reproducibility:
+
+```python
+>>> from transformers import pipeline, set_seed
+>>> generator = pipeline('text-generation', model='malteos/gpt2-xl-wechsel-german')
+>>> set_seed(42)
+>>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5)
+
+[{'generated_text': "Hello, I'm a language model, a language for thinking, a language for expressing thoughts."},
+ {'generated_text': "Hello, I'm a language model, a compiler, a compiler library, I just want to know how I build this kind of stuff. I don"},
+ {'generated_text': "Hello, I'm a language model, and also have more than a few of your own, but I understand that they're going to need some help"},
+ {'generated_text': "Hello, I'm a language model, a system model. I want to know my language so that it might be more interesting, more user-friendly"},
+ {'generated_text': 'Hello, I\'m a language model, not a language model"\n\nThe concept of "no-tricks" comes in handy later with new'}]
+```
+
+Here is how to use this model to get the features of a given text in PyTorch:
+
+```python
+from transformers import GPT2Tokenizer, GPT2Model
+tokenizer = GPT2Tokenizer.from_pretrained('malteos/gpt2-xl-wechsel-german')
+model = GPT2Model.from_pretrained('malteos/gpt2-xl-wechsel-german')
+text = "Replace me by any text you'd like."
+encoded_input = tokenizer(text, return_tensors='pt')
+output = model(**encoded_input)
+```
+
 ## Evaluation
 
 | Model (size) | PPL |