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*This model was released on 2019-07-29 and added to Hugging Face Transformers on 2020-11-16 and contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten).*

# BertGeneration

[BertGeneration](https://huggingface.co/papers/1907.12461) leverages pre-trained BERT checkpoints for sequence-to-sequence tasks using an EncoderDecoderModel framework. This approach achieves state-of-the-art results in Machine Translation, Text Summarization, Sentence Splitting, and Sentence Fusion, demonstrating the utility of initializing both encoder and decoder with pre-trained models.

<hfoptions id="usage">
<hfoption id="Pipeline">

```py
import torch
from transformers import pipeline

pipeline = pipeline(task="text2text-generation", model="google/bert_for_seq_generation_L-24_bbc_encoder", dtype="auto")
pipeline("Plants generate energy through a process known as  ")
```

</hfoption>
<hfoption id="AutoModel">

```py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder", dtype="auto")
tokenizer = AutoTokenizer.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder")

inputs = tokenizer("Plants generate energy through a process known as  ", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))
```

</hfoption>
</hfoptions>

## Usage tips

- Use [`BertGenerationEncoder`] and [`BertGenerationDecoder`] with [`EncoderDecoderModel`] for sequence-to-sequence tasks.
- Summarization, sentence splitting, sentence fusion, and translation don't require special tokens in the input.
- Don't add `EOS` tokens to the end of inputs for most generation tasks.

## BertGenerationConfig

[[autodoc]] BertGenerationConfig

## BertGenerationTokenizer

[[autodoc]] BertGenerationTokenizer
    - save_vocabulary

## BertGenerationEncoder

[[autodoc]] BertGenerationEncoder
    - forward

## BertGenerationDecoder

[[autodoc]] BertGenerationDecoder
    - forward
