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*This model was released on 2020-07-28 and added to Hugging Face Transformers on 2021-05-07 and contributed by [vasudevgupta](https://huggingface.co/vasudevgupta).*

# BigBirdPegasus

[BigBird: Transformers for Longer Sequences](https://huggingface.co/papers/2007.14062) introduces a sparse-attention mechanism that reduces the quadratic dependency on sequence length to linear, enabling handling of much longer sequences compared to models like BERT. BigBird combines sparse, global, and random attention to approximate full attention efficiently. This allows it to process sequences up to 8 times longer on similar hardware, improving performance on long document NLP tasks such as question answering and summarization. The model is also a universal approximator of sequence functions and Turing complete, preserving the capabilities of full attention models. Additionally, BigBird explores applications in genomics data.

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

```py
import torch
from transformers import pipeline

pipeline = pipeline(task="summarization", model="google/bigbird-pegasus-large-arxiv", dtype="auto")
pipeline("Plants are among the most remarkable and essential life forms on Earth, possessing a unique ability to produce their own food through a process known as photosynthesis. This complex biochemical process is fundamental not only to plant life but to virtually all life on the planet. Through photosynthesis, plants capture energy from sunlight using a green pigment called chlorophyll, which is located in specialized cell structures called chloroplasts. In the presence of light, plants absorb carbon dioxide from the atmosphere through small pores in their leaves called stomata, and take in water from the soil through their root systems. These ingredients are then transformed into glucose, a type of sugar that serves as a source of chemical energy, and oxygen, which is released as a byproduct into the atmosphere. The glucose produced during photosynthesis is not just used immediately; plants also store it as starch or convert it into other organic compounds like cellulose, which is essential for building their cellular structure.")
```

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

```py
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model = AutoModelForSeq2SeqLM.from_pretrained("google/bigbird-pegasus-large-arxiv", dtype="auto")
tokenizer = AutoTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv")

text="""
Plants are among the most remarkable and essential life forms on Earth, possessing a unique ability to produce their own food through a process known as photosynthesis. This complex biochemical process is fundamental not only to plant life but to virtually all life on the planet.
Through photosynthesis, plants capture energy from sunlight using a green pigment called chlorophyll, which is located in specialized cell structures called chloroplasts. In the presence of light, plants absorb carbon dioxide from the atmosphere through small pores in their leaves called stomata, and take in water from the soil through their root systems.
These ingredients are then transformed into glucose, a type of sugar that serves as a source of chemical energy, and oxygen, which is released as a byproduct into the atmosphere. The glucose produced during photosynthesis is not just used immediately; plants also store it as starch or convert it into other organic compounds like cellulose, which is essential for building their cellular structure.
"""
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))
```

</hfoption>
</hfoptions>

## Usage tips

- BigBirdPegasus uses [`PegasusTokenizer`].
- Pad inputs on the right. BigBird uses absolute position embeddings.
- BigBirdPegasus supports `original_full` and `block_sparse` attention. Use `original_full` for sequences under 1024 tokens since sparse patterns don't help much with smaller inputs.
- Current implementation uses 3-block window size and 2 global blocks. It only supports ITC-implementation and doesn't support `num_random_blocks=0`.
- Sequence length must be divisible by the block size.

## BigBirdPegasusConfig

[[autodoc]] BigBirdPegasusConfig
    - all

## BigBirdPegasusModel

[[autodoc]] BigBirdPegasusModel
    - forward

## BigBirdPegasusForConditionalGeneration

[[autodoc]] BigBirdPegasusForConditionalGeneration
    - forward

## BigBirdPegasusForSequenceClassification

[[autodoc]] BigBirdPegasusForSequenceClassification
    - forward

## BigBirdPegasusForQuestionAnswering

[[autodoc]] BigBirdPegasusForQuestionAnswering
    - forward

## BigBirdPegasusForCausalLM

[[autodoc]] BigBirdPegasusForCausalLM
    - forward

