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---
language: no
license: cc-by-4.0
tags:
- translation
datasets:
- oscar
widget:
- text: Skriv inn en tekst som du ønsker å oversette til en annen målform.
---
# Norwegian T5 - Translation Bokmål Nynorsk - Development

## Description

This is the development version of the Bokmål-Nynorsk translator. If you want something that is stable, Please do run [this version](https://huggingface.co/pere/nb-nn-translation/) instead.


Here is an example of how to use the model from Python
```python
# Import libraries
from transformers import T5ForConditionalGeneration, AutoTokenizer
model = T5ForConditionalGeneration.from_pretrained('pere/nb-nn-dev',from_flax=True)
tokenizer = AutoTokenizer.from_pretrained('pere/nb-nn-dev')
#Encode the text
text = "Hun vil ikke gi bort sine personlige data."
inputs = tokenizer.encode(text, return_tensors="pt")
outputs = model.generate(inputs, max_length=255, num_beams=4, early_stopping=True)
#Decode and print the result
print(tokenizer.decode(outputs[0]))
```

Or if you like to use the pipeline instead

```python
# Set up the pipeline
from transformers import pipeline
translator = pipeline("translation", model='pere/nb-nn-dev')
# Do the translation
text = "Hun vil ikke gi bort sine personlige data."
print(translator(text, max_length=255))
```

	---
	language: no
	license: cc-by-4.0
	tags:
	- translation
	datasets:
	- oscar
	widget:
	- text: Skriv inn en tekst som du ønsker å oversette til en annen målform.
	---
	# Norwegian T5 - Translation Bokmål Nynorsk - Development

	## Description

	This is the development version of the Bokmål-Nynorsk translator. If you want something that is stable, Please do run [this version](https://huggingface.co/pere/nb-nn-translation/) instead.


	Here is an example of how to use the model from Python
	```python
	# Import libraries
	from transformers import T5ForConditionalGeneration, AutoTokenizer
	model = T5ForConditionalGeneration.from_pretrained('pere/nb-nn-dev',from_flax=True)
	tokenizer = AutoTokenizer.from_pretrained('pere/nb-nn-dev')

	#Encode the text
	text = "Hun vil ikke gi bort sine personlige data."
	inputs = tokenizer.encode(text, return_tensors="pt")
	outputs = model.generate(inputs, max_length=255, num_beams=4, early_stopping=True)

	#Decode and print the result
	print(tokenizer.decode(outputs[0]))

	```

	Or if you like to use the pipeline instead

	```python
	# Set up the pipeline
	from transformers import pipeline
	translator = pipeline("translation", model='pere/nb-nn-dev')

	# Do the translation
	text = "Hun vil ikke gi bort sine personlige data."
	print(translator(text, max_length=255))

	```