README.md

---
language:
- en
license: apache-2.0
tags:
- summarization
- pegasus
datasets:
- kmfoda/booksum
metrics:
- rouge
widget:
- text: large earthquakes along a given fault segment do not occur at random intervals
    because it takes time to accumulate the strain energy for the rupture. The rates
    at which tectonic plates move and accumulate strain at their boundaries are approximately
    uniform. Therefore, in first approximation, one may expect that large ruptures
    of the same fault segment will occur at approximately constant time intervals.
    If subsequent main shocks have different amounts of slip across the fault, then
    the recurrence time may vary, and the basic idea of periodic mainshocks must be
    modified. For great plate boundary ruptures the length and slip often vary by
    a factor of 2. Along the southern segment of the San Andreas fault the recurrence
    interval is 145 years with variations of several decades. The smaller the standard
    deviation of the average recurrence interval, the more specific could be the long
    term prediction of a future mainshock.
  example_title: earthquakes
- text: ' A typical feed-forward neural field algorithm. Spatiotemporal coordinates
    are fed into a neural network that predicts values in the reconstructed domain.
    Then, this domain is mapped to the sensor domain where sensor measurements are
    available as supervision. Class and Section Problems Addressed Generalization
    (Section 2) Inverse problems, ill-posed problems, editability; symmetries. Hybrid
    Representations (Section 3) Computation & memory efficiency, representation capacity,
    editability: Forward Maps (Section 4) Inverse problems Network Architecture (Section
    5) Spectral bias, integration & derivatives. Manipulating Neural Fields (Section
    6) Edit ability, constraints, regularization. Table 2: The five classes of techniques
    in the neural field toolbox each addresses problems that arise in learning, inference,
    and control. (Section 3). We can supervise reconstruction via differentiable forward
    maps that transform Or project our domain (e.g, 3D reconstruction via 2D images;
    Section 4) With appropriate network architecture choices, we can overcome neural
    network spectral biases (blurriness) and efficiently compute derivatives and integrals
    (Section 5). Finally, we can manipulate neural fields to add constraints and regularizations,
    and to achieve editable representations (Section 6). Collectively, these classes
    constitute a ''toolbox'' of techniques to help solve problems with neural fields
    There are three components in a conditional neural field: (1) An encoder or inference
    function € that outputs the conditioning latent variable 2 given an observation
    0 E(0) =2. 2 is typically a low-dimensional vector, and is often referred to aS
    a latent code Or feature code_ (2) A mapping function 4 between Z and neural field
    parameters O: Y(z) = O; (3) The neural field itself $. The encoder € finds the
    most probable z given the observations O: argmaxz P(2/0). The decoder maximizes
    the inverse conditional probability to find the most probable 0 given Z: arg-
    max P(Olz). We discuss different encoding schemes with different optimality guarantees
    (Section 2.1.1), both global and local conditioning (Section 2.1.2), and different
    mapping functions Y (Section 2.1.3) 2. Generalization Suppose we wish to estimate
    a plausible 3D surface shape given a partial or noisy point cloud. We need a suitable
    prior over the sur- face in its reconstruction domain to generalize to the partial
    observations. A neural network expresses a prior via the function space of its
    architecture and parameters 0, and generalization is influenced by the inductive
    bias of this function space (Section 5).'
  example_title: scientific paper
- text: ' the big variety of data coming from diverse sources is one of the key properties
    of the big data phenomenon. It is, therefore, beneficial to understand how data
    is generated in various environments and scenarios, before looking at what should
    be done with this data and how to design the best possible architecture to accomplish
    this The evolution of IT architectures, described in Chapter 2, means that the
    data is no longer processed by a few big monolith systems, but rather by a group
    of services In parallel to the processing layer, the underlying data storage has
    also changed and became more distributed This, in turn, required a significant
    paradigm shift as the traditional approach to transactions (ACID) could no longer
    be supported. On top of this, cloud computing is becoming a major approach with
    the benefits of reducing costs and providing on-demand scalability but at the
    same time introducing concerns about privacy, data ownership, etc In the meantime
    the Internet continues its exponential growth: Every day both structured and unstructured
    data is published and available for processing: To achieve competitive advantage
    companies have to relate their corporate resources to external services, e.g.
    financial markets, weather forecasts, social media, etc While several of the sites
    provide some sort of API to access the data in a more orderly fashion; countless
    sources require advanced web mining and Natural Language Processing (NLP) processing
    techniques: Advances in science push researchers to construct new instruments
    for observing the universe O conducting experiments to understand even better
    the laws of physics and other domains. Every year humans have at their disposal
    new telescopes, space probes, particle accelerators, etc These instruments generate
    huge streams of data, which need to be stored and analyzed. The constant drive
    for efficiency in the industry motivates the introduction of new automation techniques
    and process optimization: This could not be done without analyzing the precise
    data that describe these processes. As more and more human tasks are automated,
    machines provide rich data sets, which can be analyzed in real-time to drive efficiency
    to new levels. Finally, it is now evident that the growth of the Internet of Things
    is becoming a major source of data. More and more of the devices are equipped
    with significant computational power and can generate a continuous data stream
    from their sensors. In the subsequent sections of this chapter, we will look at
    the domains described above to see what they generate in terms of data sets. We
    will compare the volumes but will also look at what is characteristic and important
    from their respective points of view. 3.1 The Internet is undoubtedly the largest
    database ever created by humans. While several well described; cleaned, and structured
    data sets have been made available through this medium, most of the resources
    are of an ambiguous, unstructured, incomplete or even erroneous nature. Still,
    several examples in the areas such as opinion mining, social media analysis, e-governance,
    etc, clearly show the potential lying in these resources. Those who can successfully
    mine and interpret the Internet data can gain unique insight and competitive advantage
    in their business An important area of data analytics on the edge of corporate
    IT and the Internet is Web Analytics.'
  example_title: data science textbook
- text: 'Transformer-based models have shown to be very useful for many NLP tasks.
    However, a major limitation of transformers-based models is its O(n^2)O(n 2) time
    & memory complexity (where nn is sequence length). Hence, it''s computationally
    very expensive to apply transformer-based models on long sequences n > 512n>512.
    Several recent papers, e.g. Longformer, Performer, Reformer, Clustered attention
    try to remedy this problem by approximating the full attention matrix. You can
    checkout 🤗''s recent blog post in case you are unfamiliar with these models.

    BigBird (introduced in paper) is one of such recent models to address this issue.
    BigBird relies on block sparse attention instead of normal attention (i.e. BERT''s
    attention) and can handle sequences up to a length of 4096 at a much lower computational
    cost compared to BERT. It has achieved SOTA on various tasks involving very long
    sequences such as long documents summarization, question-answering with long contexts.

    BigBird RoBERTa-like model is now available in 🤗Transformers. The goal of this
    post is to give the reader an in-depth understanding of big bird implementation
    & ease one''s life in using BigBird with 🤗Transformers. But, before going into
    more depth, it is important to remember that the BigBird''s attention is an approximation
    of BERT''s full attention and therefore does not strive to be better than BERT''s
    full attention, but rather to be more efficient. It simply allows to apply transformer-based
    models to much longer sequences since BERT''s quadratic memory requirement quickly
    becomes unbearable. Simply put, if we would have ∞ compute & ∞ time, BERT''s attention
    would be preferred over block sparse attention (which we are going to discuss
    in this post).

    If you wonder why we need more compute when working with longer sequences, this
    blog post is just right for you!

    Some of the main questions one might have when working with standard BERT-like
    attention include:

    Do all tokens really have to attend to all other tokens? Why not compute attention
    only over important tokens? How to decide what tokens are important? How to attend
    to just a few tokens in a very efficient way? In this blog post, we will try to
    answer those questions.

    What tokens should be attended to? We will give a practical example of how attention
    works by considering the sentence ''BigBird is now available in HuggingFace for
    extractive question answering''. In BERT-like attention, every word would simply
    attend to all other tokens.

    Let''s think about a sensible choice of key tokens that a queried token actually
    only should attend to by writing some pseudo-code. Will will assume that the token
    available is queried and build a sensible list of key tokens to attend to.

    >>> # let''s consider following sentence as an example >>> example = [''BigBird'',
    ''is'', ''now'', ''available'', ''in'', ''HuggingFace'', ''for'', ''extractive'',
    ''question'', ''answering'']

    >>> # further let''s assume, we''re trying to understand the representation of
    ''available'' i.e. >>> query_token = ''available'' >>> # We will initialize an
    empty `set` and fill up the tokens of our interest as we proceed in this section.
    >>> key_tokens = [] # => currently ''available'' token doesn''t have anything
    to attend Nearby tokens should be important because, in a sentence (sequence of
    words), the current word is highly dependent on neighboring past & future tokens.
    This intuition is the idea behind the concept of sliding attention.'
  example_title: bigbird blog intro
inference:
  parameters:
    max_length: 64
    no_repeat_ngram_size: 2
    encoder_no_repeat_ngram_size: 3
    repetition_penalty: 2.4
    length_penalty: 0.5
    num_beams: 4
    early_stopping: true
model-index:
- name: pszemraj/pegasus-large-summary-explain
  results:
  - task:
      type: summarization
      name: Summarization
    dataset:
      name: kmfoda/booksum
      type: kmfoda/booksum
      config: kmfoda--booksum
      split: test
    metrics:
    - type: rouge
      value: 29.1023
      name: ROUGE-1
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYTFhNjg4YTFlODU5MmVjNGVmNDRmMjQ4M2YyZGNmMWRlYjBhZmVhMTY3ZTUxNDkzNjY0OGVmNWJlNmY1OTkzNCIsInZlcnNpb24iOjF9.E_rVKqB7WEerLeRq6JIVTLZ1TgmsThFQJVKh11WH1qWa-cL3766psPWDKe8mK3lNkjmwbiDW0DZlDt4dm2ATCA
    - type: rouge
      value: 6.2441
      name: ROUGE-2
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDVmZmFlOTgwN2Q3ZWRkZGVkMzU1ZDRkYzU1MWMzMTk1NDM5YTU0MzFjNDljNmZlY2I2NjZmZjcyYjBkZGExZCIsInZlcnNpb24iOjF9.QnuGoMWX8cq5_ukRtiaLRLau_F9XiCjg313GC7Iu1VGK8Kj_9lzU43377VsH0fBWooA1zJjtIK0UA-YpGQQOAA
    - type: rouge
      value: 14.7503
      name: ROUGE-L
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzJhNzE0YjZiZWQ4NDE1Yjg3ZGJjY2ZmYWEwYzU5MTRhYWNiNTcyODU1NzM5NTZhNjNlNmYwNDVlYmZmYjkxOCIsInZlcnNpb24iOjF9.m5BLUMefXa1KivIIE9-gYKYq5aRRbfpQWazqzXxfCsqqp38Lt0ymk6OwXSlQyB_5oksNHIDFKpJX4wjYx2i7Bw
    - type: rouge
      value: 27.2375
      name: ROUGE-LSUM
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTY1OTIxMzBkMGJiZmNiNjZjYmQ2MjUwMjBkYTg5Zjc1NjVlZjllNTg0MDM1NTdhZDJlZmIwOTczOGNkZDc5YyIsInZlcnNpb24iOjF9.bThI16mvqhEuGBhdao0w8j03vv9G9Quy-ITRZzalr41zOour9it4oxEPFCvmPf-nLCQkqgWKUDEzgr6Ww8qgBg
    - type: loss
      value: 2.979011058807373
      name: loss
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOGM0NzM3YTI4Njg4NDY0ZjQzNTZmYTIxYzcxNDBlNzAwNTAxNDE4MTZjYmZmNzYwODU0OWQ1ZjM5YjRmMmFkZiIsInZlcnNpb24iOjF9.EPEP53AoqHz0rjVGStJI2dM7ivxFmOj572I3llWdAoejm3zO1Iq5WDArYsqOse_oLxYCgcqPmNVc5IcLW9x7Dg
    - type: gen_len
      value: 467.269
      name: gen_len
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNjgzYzU2ZjkwN2RhNzJlZmQyZTBlYmUxMTZhNzg0ODMwMjA3OTUzNTIwOWFkZWVmNjVmMTJiZmZhNWFmY2UzZCIsInZlcnNpb24iOjF9.RW5tzk2fcc_m4bgaSopRDFhSR9R8hRaYKrstXH4X5iGP_Xwvhy5Q7-igd2ACnlxIfmtdTmMxLMsvHr5oAZEwDg
---


# pszemraj/pegasus-large-summary-explain

This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on the [booksum](https://github.com/salesforce/booksum) dataset for four total epochs.

It achieves the following results on the evaluation set:
- eval_loss: 1.1193
- eval_runtime: 6.6754
- eval_samples_per_second: 27.714
- eval_steps_per_second: 1.798
- epoch: 3.0
- step: 900

A 1-epoch checkpoint can be found at [pszemraj/pegasus-large-book-summary](https://huggingface.co/pszemraj/pegasus-large-book-summary), which is where the second training session started from.

## Model description

- After some initial tests, it was found that models trained on the [booksum](https://github.com/salesforce/booksum) dataset seem to inherit the summaries' SparkNotes-style explanations; so the user gets a shorter and easier-to-understand version of the text instead of **just** more compact. 
 - This quality (anecdotally) is favourable for learning/comprehension because summarization datasets that simply make the information more compact (* cough * arXiv) can be so dense that the overall time spent trying to _comprehend_ what it is saying can be the same as just reading the original material.


## Intended uses & limitations

- standard pegasus has a max input length of 1024 tokens, therefore the model only saw the first 1024 tokens of a chapter when training, and learned to try to make the chapter's summary from that. Keep this in mind when using this model, as information at the end of a text sequence longer than 1024 tokens may be excluded from the final summary/the model will be biased towards information presented first.

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 4

### Framework versions

- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0