
# 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? .
```

{"language": ["en", "de"], "license": "apache-2.0", "tags": ["translation"], "datasets": ["wmt14"]}
transformers pytorch encoder-decoder text2text-generation translation en de dataset:wmt14 arxiv:1907.12461 license:apache-2.0 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #translation #en #de #dataset-wmt14 #arxiv-1907.12461 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 


# bert2bert_L-24_wmt_de_en EncoderDecoder model

The model was introduced in 
this paper by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in this repository. 

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.*



# bert2bert_L-24_wmt_de_en EncoderDecoder model

The model was introduced in 
this paper by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in this repository. 

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.*
TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #translation #en #de #dataset-wmt14 #arxiv-1907.12461 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # bert2bert_L-24_wmt_de_en EncoderDecoder model

The model was introduced in 
this paper by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in this repository. 

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.*
TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #translation #en #de #dataset-wmt14 #arxiv-1907.12461 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
# bert2bert_L-24_wmt_de_en EncoderDecoder model

The model was introduced in 
this paper by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in this repository. 

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.*