pytorch encoder-decoder seq2seq en de dataset:wmt14 arxiv:1907.12461 transformers license:apache-2.0 translation text2text-generation
.gitattributes README.md config.json pytorch_model.bin vocab.txt
---
language: 
- en
- de
license: apache-2.0
datasets:
- wmt14
tags:
- translation
---

# bert2bert_L-24_wmt_de_en EncoderDecoder model

The model was introduced in 
[this paper](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in [this repository](https://tfhub.dev/google/bertseq2seq/bert24_de_en/1). 

The model is an encoder-decoder model that was initialized on the `bert-large` checkpoints for both the encoder 
and decoder and fine-tuned on German to English translation on the WMT dataset, which is linked above.

Disclaimer: The model card has been written by the Hugging Face team.

## How to use

You can use this model for translation, *e.g.*

```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("google/bert2bert_L-24_wmt_de_en", pad_token="<pad>", eos_token="</s>", bos_token="<s>")
model = AutoModelForSeq2SeqLM.from_pretrained("google/bert2bert_L-24_wmt_de_en")

sentence = "Willst du einen Kaffee trinken gehen mit mir?"

input_ids = tokenizer(sentence, return_tensors="pt", add_special_tokens=False).input_ids
output_ids = model.generate(input_ids)[0]
print(tokenizer.decode(output_ids, skip_special_tokens=True))
# should output
# Want to drink a kaffee go with me? .
```
