Translation
Transformers
PyTorch
JAX
TensorBoard
Norwegian
t5
text2text-generation
text-generation-inference
Instructions to use north/demo-deuncaser-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use north/demo-deuncaser-base with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="north/demo-deuncaser-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("north/demo-deuncaser-base") model = AutoModelForSeq2SeqLM.from_pretrained("north/demo-deuncaser-base") - Notebooks
- Google Colab
- Kaggle
DeUnCaser
The purpose of the DeUnCaser is to fix text that lacks punctation. It is particulary targeted towards the output from Automated Speak Recognition software. In addition to the lack of casing and punctation, it also often lacks pauses between words. Try this demo, and you will understand.
The DeUnCaser is based on North-T5. It is a sequence-to-sequence mT5 model. It will make an attempt to add punctation, spaces and capitalisation to any text that is thrown at it. It is primarily trained to fix Norwegian text.
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