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*This model was released on 2020-01-13 and added to Hugging Face Transformers on 2020-11-16.*

# ProphetNet

[ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://huggingface.co/papers/2001.04063) is an encoder-decoder model that employs future n-gram prediction and an n-stream self-attention mechanism. Unlike traditional models that predict the next token, ProphetNet predicts the next n tokens simultaneously, enhancing its ability to plan for future tokens and reducing overfitting on local correlations. Pre-trained on both base (16GB) and large (160GB) datasets, ProphetNet outperforms other models on CNN/DailyMail, Gigaword, and SQuAD 1.1 benchmarks for abstractive summarization and question generation tasks.

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

```py
import torch
from transformers import pipeline

pipeline = pipeline(task="text-generation", model="microsoft/prophetnet-large-uncased", dtype="auto",)
pipeline("Plants create energy through a process known as photosynthesis.")
```

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

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

tokenizer = AutoTokenizer.from_pretrained("microsoft/prophetnet-large-uncased")
model = AutoModelForCausalLM.from_pretrained("microsoft/prophetnet-large-uncased", dtype="auto",)

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

</hfoption>
</hfoptions>

## Usage tips

- Pad inputs on the right. ProphetNet uses absolute position embeddings.
- The model architecture is based on the original Transformer. It replaces the "standard" self-attention mechanism in the decoder with a main self-attention mechanism and a self and n-stream (predict) self-attention mechanism.

## ProphetNetConfig

[[autodoc]] ProphetNetConfig

## ProphetNetTokenizer

[[autodoc]] ProphetNetTokenizer

## ProphetNet specific outputs

[[autodoc]] models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqLMOutput

[[autodoc]] models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqModelOutput

[[autodoc]] models.prophetnet.modeling_prophetnet.ProphetNetDecoderModelOutput

[[autodoc]] models.prophetnet.modeling_prophetnet.ProphetNetDecoderLMOutput

## ProphetNetModel

[[autodoc]] ProphetNetModel
    - forward

## ProphetNetEncoder

[[autodoc]] ProphetNetEncoder
    - forward

## ProphetNetDecoder

[[autodoc]] ProphetNetDecoder
    - forward

## ProphetNetForConditionalGeneration

[[autodoc]] ProphetNetForConditionalGeneration
    - forward

## ProphetNetForCausalLM

[[autodoc]] ProphetNetForCausalLM
    - forward

