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
metadata
language:
- de
- en
datasets:
- wmt14
pipeline_tag: translation
model-index:
- name: leukas/byt5-large-wmt14-deen
results:
- task:
type: translation
name: Translation
dataset:
name: wmt14
type: wmt14
config: de-en
split: test
metrics:
- type: bleu
value: 0.236
name: BLEU
verified: true
verifyToken: >-
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- type: loss
value: 0.3008817732334137
name: loss
verified: true
verifyToken: >-
eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzIyM2I2Yjg1NWQ2NjEzOTAwN2NlNDc3NWU4NmI3ZDFhYzZlMTQyNjZlMWM0Njg5YWIzMmI5MzBjN2Y3NmMzYSIsInZlcnNpb24iOjF9.2totcDoHCyf7N9xaqAFVqlyuaoHM3hRIH5jmP4kPoD0crEcPZ0re6pg10_2Uoud4YunWMRvOpUTt8lk2cCM1BA
- type: gen_len
value: 20
name: gen_len
verified: true
verifyToken: >-
eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzdjODYyY2UxZGZjNmFmZGFlY2IzNzQxNzA3YjQ3MDg1OWY3YjdkMzM2NGI2Njk2ZDczODNlOWM4YjZjZDQ2MCIsInZlcnNpb24iOjF9.hRVI7QHiBlX3Yp0cFOdezmoxV6CTq20vzp8IzugYc0uTUVD5OAvcBVRLZERGoNR1b1Oi2FV3trQvDWXUQZnsAQ
byt5-large-wmt14-deen
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. It is a ByT5 model finetuned on German-->English translation the WMT14 dataset.
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).
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).