README.md

---
license:
- bsd-3-clause
- apache-2.0
tags:
- generated_from_trainer
- lay summary
- narrative
- biomedical
- long document summary
metrics:
- rouge
datasets:
- pszemraj/scientific_lay_summarisation-elife-norm
language:
- en
library_name: transformers
pipeline_tag: summarization
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
  - text: >-
      The tower is 324 metres (1,063 ft) tall, about the same height as an
      81-storey building, and the tallest structure in Paris. Its base is
      square, measuring 125 metres (410 ft) on each side. During its
      construction, the Eiffel Tower surpassed the Washington Monument to become
      the tallest man-made structure in the world, a title it held for 41 years
      until the Chrysler Building in New York City was finished in 1930. It was
      the first structure to reach a height of 300 metres. Due to the addition
      of a broadcasting aerial at the top of the tower in 1957, it is now taller
      than the Chrysler Building by 5.2 metres (17 ft). Excluding transmitters,
      the Eiffel Tower is the second tallest free-standing structure in France
      after the Millau Viaduct.
    example_title: eiffel
parameters:
  max_length: 64
  min_length: 8
  no_repeat_ngram_size: 3
  early_stopping: true
  repetition_penalty: 3.5
  encoder_no_repeat_ngram_size: 4
  length_penalty: 0.4
  num_beams: 4
---


# long-t5-tglobal-base-16384-booksci-summary: v1

<a href="https://colab.research.google.com/gist/pszemraj/ee06b2b3bfcd7d29e588697a90f7a776/long-t5-tglobal-base-16384-booksci-summary-v1-example-with-textsum.ipynb">
  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>

An experiment investigating transfer learning capabilities by fine-tuning models on different datasets starting from the `booksum` checkpoint.


## Model Details

This model is a fine-tuned version of [pszemraj/long-t5-tglobal-base-16384-book-summary](https://huggingface.co/pszemraj/long-t5-tglobal-base-16384-book-summary) on the `pszemraj/scientific_lay_summarisation-elife-norm` dataset for two epochs.



## Usage 

It's recommended to use this model with [beam search decoding](https://huggingface.co/docs/transformers/generation_strategies#beamsearch-decoding). If interested, you can also use the `textsum` util repo to have most of this abstracted out for you:


```bash
pip install -U textsum
```

```python
from textsum.summarize import Summarizer

model_name = "pszemraj/long-t5-tglobal-base-16384-booksci-summary-v1"
summarizer = Summarizer(model_name) # GPU auto-detected
text = "put the text you don't want to read here"
summary = summarizer.summarize_string(text)
print(summary)
```


## Intended uses & limitations

- This is an initial experiment
- Domain generalization abilities at time of writing are unknown

## Training procedure

> Note: this model was trained at a lower LR & not till "absolute convergence" with the intention of retaining some of the properties learned from the initial fine-tuning on `booksum`

### Results 

It achieves the following results on the evaluation set:
- Loss: 2.3994
- Rouge1: 34.2428
- Rouge2: 4.3644
- Rougel: 12.5332
- Rougelsum: 30.6965
- Gen Len: 294.0249

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 2.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2 | Rougel  | Rougelsum | Gen Len  |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:--------:|
| 2.7492        | 0.99  | 67   | 2.4272          | 34.6436 | 4.4536 | 12.4985 | 30.916    | 300.7635 |
| 2.6689        | 1.97  | 134  | 2.3994          | 34.2428 | 4.3644 | 12.5332 | 30.6965   | 294.0249 |