language: de | |
widget: | |
- text: My name is Karl and I live in Aachen. | |
tags: | |
- translation | |
datasets: | |
- wmt19 | |
license: gpl | |
model-index: | |
- name: Tanhim/translation-En2De | |
results: | |
- task: | |
type: translation | |
name: Translation | |
dataset: | |
name: wmt19 | |
type: wmt19 | |
config: de-en | |
split: validation | |
metrics: | |
- name: BLEU | |
type: bleu | |
value: 43.3134 | |
verified: true | |
- name: loss | |
type: loss | |
value: 0.919737696647644 | |
verified: true | |
- name: gen_len | |
type: gen_len | |
value: 27.8909 | |
verified: true | |
<h2> English to German Translation </h2> | |
Model Name: Tanhim/translation-En2De <br /> | |
language: German or Deutsch <br /> | |
thumbnail: https://huggingface.co/Tanhim/translation-En2De <br /> | |
### How to use | |
You can use this model directly with a pipeline for machine translation. Since the generation relies on some randomness, I | |
set a seed for reproducibility: | |
```python | |
>>> from transformers import pipeline, set_seed | |
>>> text_En2De= pipeline('translation', model='Tanhim/translation-En2De', tokenizer='Tanhim/translation-En2De') | |
>>> set_seed(42) | |
>>> text_En2De("My name is Karl and I live in Aachen") | |
``` | |
### beta version |