Translation
Transformers
PyTorch
Safetensors
German
English
t5
text2text-generation
Eval Results (legacy)
text-generation-inference
Instructions to use leukas/byt5-large-wmt14-deen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use leukas/byt5-large-wmt14-deen 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="leukas/byt5-large-wmt14-deen")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("leukas/byt5-large-wmt14-deen") model = AutoModelForMultimodalLM.from_pretrained("leukas/byt5-large-wmt14-deen") - Notebooks
- Google Colab
- Kaggle
Create README.md
Browse files
README.md
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---
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language:
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- de
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- en
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pipeline_tag: translation
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datasets:
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- wmt14
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---
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# byt5-large-wmt14-deen
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This model is released as part of the work from [Are Character-level Translations Worth the Wait? Comparing Character- and Subword-level Models for Machine Translation](https://arxiv.org/abs/2302.14220).
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It is a ByT5 model finetuned on German-->English translation the WMT14 dataset.
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To use the model correctly, you must prepend the prompt with "translate X to Y: ", where X and Y are your source and target languages (e.g. German, English).
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NOTE: The decoder_start_token_id is 259 for byt5 models and 250099 for mt5 models, which is different from the default token from google's byt5 and mt5 models (which is 0).
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