| 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 |