multi-task mixture dataset (including WMT), yet, yielding impressive translation results.
```py
from transformers import pipeline
translator = pipeline("translation_en_to_de")
print(translator("Hugging Face is a technology company based in New York and Paris", max_length=40))
[{'translation_text': 'Hugging Face ist ein Technologieunternehmen mit Sitz in New York und Paris.'}]
```
Because the translation pipeline depends on the PreTrainedModel.generate() method, we can override the default
arguments of PreTrainedModel.generate() directly in the pipeline as is shown for max_length above.
Here is an example of doing translation using a model and a tokenizer. The process is the following:
Instantiate a tokenizer and a model from the checkpoint name. Summarization is usually done using an encoder-decoder
