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tags:
  - summarization
  - summary
  - booksum
  - long-document
  - long-form
license: apache-2.0
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: >-
      Is a else or outside the cob and tree written being of early client rope
      and you have is for good reasons. On to the ocean in Orange for time. By's
      the aggregate we can bed it yet. Why this please pick up on a sort is do
      and also M Getoi's nerocos and do rain become you to let so is his brother
      is made in use and Mjulia's's the lay major is aging Masastup coin present
      sea only of Oosii rooms set to you We do er do we easy this private
      oliiishs lonthen might be okay. Good afternoon everybody. Welcome to this
      lecture of Computational Statistics. As you can see, I'm not socially my
      name is Michael Zelinger. I'm one of the task for this class and you might
      have already seen me in the first lecture where I made a quick appearance.
      I'm also going to give the tortillas in the last third of this course. So
      to give you a little bit about me, I'm a old student here with better
      Bulman and my research centres on casual inference applied to biomedical
      disasters, so that could be genomics or that could be hospital data. If
      any of you is interested in writing a bachelor thesis, a semester paper
      may be mastathesis about this topic feel for reach out to me. you have my
      name on models and my email address you can find in the directory I'd Be
      very happy to talk about it. you do not need to be sure about it, we can
      just have a chat. So with that said, let's get on with the lecture.
      There's an exciting topic today I'm going to start by sharing some slides
      with you and later on during the lecture we'll move to the paper. So bear
      with me for a few seconds. Well, the projector is starting up. Okay, so
      let's get started. Today's topic is a very important one. It's about a
      technique which really forms one of the fundamentals of data science,
      machine learning, and any sort of modern statistics. It's called cross
      validation. I know you really want to understand this topic I Want you to
      understand this and frankly, nobody's gonna leave Professor Mineshousen's
      class without understanding cross validation. So to set the stage for
      this, I Want to introduce you to the validation problem in computational
      statistics. So the problem is the following: You trained a model on
      available data. You fitted your model, but you know the training data you
      got could always have been different and some data from the environment.
      Maybe it's a random process. You do not really know what it is, but you
      know that somebody else who gets a different batch of data from the same
      environment they would get slightly different training data and you do not
      care that your method performs as well. On this training data. you want to
      to perform well on other data that you have not seen other data from the
      same environment. So in other words, the validation problem is you want to
      quantify the performance of your model on data that you have not seen. So
      how is this even possible? How could you possibly measure the performance
      on data that you do not know The solution to? This is the following
      realization is that given that you have a bunch of data, you were in
      charge. You get to control how much that your model sees. It works in the
      following way: You can hide data firms model. Let's say you have a
      training data set which is a bunch of doubtless so X eyes are the features
      those are typically hide and national vector. It's got more than one
      dimension for sure. And the why why eyes. Those are the labels for
      supervised learning. As you've seen before, it's the same set up as we
      have in regression. And so you have this training data and now you choose
      that you only use some of those data to fit your model. You're not going
      to use everything, you only use some of it the other part you hide from
      your model. And then you can use this hidden data to do validation from
      the point of you of your model. This hidden data is complete by unseen. In
      other words, we solve our problem of validation.
    example_title: transcribed audio - lecture
  - 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
  - text: >-
      To be fair, you have to have a very high IQ to understand Rick and Morty.
      The humour is extremely subtle, and without a solid grasp of theoretical
      physics most of the jokes will go over a typical viewer's head. There's
      also Rick's nihilistic outlook, which is deftly woven into his
      characterisation- his personal philosophy draws heavily from Narodnaya
      Volya literature, for instance. The fans understand this stuff; they have
      the intellectual capacity to truly appreciate the depths of these jokes,
      to realise that they're not just funny- they say something deep about
      LIFE. As a consequence people who dislike Rick & Morty truly ARE idiots-
      of course they wouldn't appreciate, for instance, the humour in Rick's
      existential catchphrase 'Wubba Lubba Dub Dub,' which itself is a cryptic
      reference to Turgenev's Russian epic Fathers and Sons. I'm smirking right
      now just imagining one of those addlepated simpletons scratching their
      heads in confusion as Dan Harmon's genius wit unfolds itself on their
      television screens. What fools.. how I pity them. 😂

      And yes, by the way, i DO have a Rick & Morty tattoo. And no, you cannot
      see it. It's for the ladies' eyes only- and even then they have to
      demonstrate that they're within 5 IQ points of my own (preferably lower)
      beforehand. Nothin personnel kid 😎
    example_title: Richard & Mortimer
parameters:
  max_length: 64
  min_length: 8
  no_repeat_ngram_size: 3
  early_stopping: true
  repetition_penalty: 3.5
  length_penalty: 0.3
  encoder_no_repeat_ngram_size: 3
  num_beams: 4
model-index:
  - name: pszemraj/long-t5-tglobal-base-16384-book-summary
    results:
      - task:
          type: summarization
          name: Summarization
        dataset:
          name: kmfoda/booksum
          type: kmfoda/booksum
          config: kmfoda--booksum
          split: test
        metrics:
          - name: ROUGE-1
            type: rouge
            value: 36.4085
            verified: true
          - name: ROUGE-2
            type: rouge
            value: 6.0646
            verified: true
          - name: ROUGE-L
            type: rouge
            value: 16.7209
            verified: true
          - name: ROUGE-LSUM
            type: rouge
            value: 33.3405
            verified: true
          - name: loss
            type: loss
            value: .nan
            verified: true
          - name: gen_len
            type: gen_len
            value: 252.8099
            verified: true
      - task:
          type: summarization
          name: Summarization
        dataset:
          name: samsum
          type: samsum
          config: samsum
          split: test
        metrics:
          - name: ROUGE-1
            type: rouge
            value: 30.9047
            verified: true
          - name: ROUGE-2
            type: rouge
            value: 7.4715
            verified: true
          - name: ROUGE-L
            type: rouge
            value: 22.3962
            verified: true
          - name: ROUGE-LSUM
            type: rouge
            value: 26.9094
            verified: true
          - name: loss
            type: loss
            value: .nan
            verified: true
          - name: gen_len
            type: gen_len
            value: 46.7973
            verified: true
      - task:
          type: summarization
          name: Summarization
        dataset:
          name: cnn_dailymail
          type: cnn_dailymail
          config: 3.0.0
          split: test
        metrics:
          - name: ROUGE-1
            type: rouge
            value: 30.5942
            verified: true
          - name: ROUGE-2
            type: rouge
            value: 7.252
            verified: true
          - name: ROUGE-L
            type: rouge
            value: 17.7156
            verified: true
          - name: ROUGE-LSUM
            type: rouge
            value: 27.2881
            verified: true
          - name: loss
            type: loss
            value: .nan
            verified: true
          - name: gen_len
            type: gen_len
            value: 125.2507
            verified: true
      - task:
          type: summarization
          name: Summarization
        dataset:
          name: xsum
          type: xsum
          config: default
          split: test
        metrics:
          - name: ROUGE-1
            type: rouge
            value: 20.3648
            verified: true
          - name: ROUGE-2
            type: rouge
            value: 3.4126
            verified: true
          - name: ROUGE-L
            type: rouge
            value: 13.6168
            verified: true
          - name: ROUGE-LSUM
            type: rouge
            value: 15.8313
            verified: true
          - name: loss
            type: loss
            value: .nan
            verified: true
          - name: gen_len
            type: gen_len
            value: 82.2177
            verified: true

long-t5-tglobal-base-16384 + BookSum

  • summarize long text and get a SparkNotes-esque summary of arbitrary topics!
  • generalizes reasonably well to academic & narrative text.
  • A very simple example/use case on ASR is here

Cheeky Proof-of-Concept

A summary of the infamous navy seals copypasta:

The narrator tells us that he's graduated from the Navy seals and has been involved in many secret raids. He's also one of the best snipers in the entire U.S. military. He promises to "wipe you out with precision" when they meet again.

Model description

A fine-tuned version of google/long-t5-tglobal-base on the kmfoda/booksum dataset:

  • 30+ epochs of fine-tuning from the base model on V100/A100 GPUs
  • all training used 16384 token input / 1024 max output

Read the paper by Guo et al. here: LongT5: Efficient Text-To-Text Transformer for Long Sequences

How-To in Python

Install/update transformers pip install -U transformers

Summarize text with pipeline:

from transformers import pipeline

summarizer = pipeline(
    'summarization',
    'pszemraj/long-t5-tglobal-base-16384-book-summary',
    )
long_text = "Here is a lot of text I don't want to read. Replace me"

result = summarizer(long_text)
print(result[0]['summary_text'])

Pass other parameters related to beam search textgen when calling summarizer to get even higher quality results.

Intended uses & limitations

  • The current checkpoint is fairly well converged but will be updated if further improvements can be made.
    • Compare performance to LED-base trained on the same dataset (API gen parameters are the same).
  • while this model seems to improve upon factual consistency, do not take summaries to be foolproof and check things that seem odd.

Training and evaluation data

kmfoda/booksum dataset on HuggingFace - read the original paper here. Summaries longer than 1024 LongT5 tokens were filtered out to prevent the model from learning to generate "partial" summaries.

NOTE: early checkpoints of this model were trained on a "smaller" subsection of the dataset as it was filtered for summaries of 1024 characters. This was subsequently caught and adjusted to 1024 tokens and then trained further for 10+ epochs.

Training procedure

Updates:

  • July 22, 2022: updated to a fairly converged checkpoint
  • July 3, 2022: Added a new version with several epochs of additional training that is more performant in general.

Training hyperparameters

The following hyperparameters were used during the most recent training round*:

  • learning_rate: 0.0005
  • train_batch_size: 1
  • eval_batch_size: 1
  • seed: 42
  • distributed_type: multi-GPU
  • gradient_accumulation_steps: 128
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.01
  • num_epochs: 2

*Prior training sessions used roughly similar parameters; multiple sessions were required as this takes aeons to train

Training results

Framework versions

  • Transformers 4.20.1
  • Pytorch 1.10.0+cu113
  • Datasets 2.3.2
  • Tokenizers 0.12.1