README.md

---
license: apache-2.0
tags:
- bio
- medical
- clinical
- literature
- keywords
- domain classifier
metrics:
- rouge
model-index:
- name: long-t5-tglobal-base-scientific_lay_summarisation-plos-norm-kw
  results: []
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: >-
    Dear Calvin,

    I was in the woods of Big Sur, that vast and sprawling land of sea and
    trees, where the wind whispers secrets of the ancient Earth and the roaring
    ocean sings songs of the eternal cosmos, when I found myself emerging from
    the deepest and darkest of slumbers, my body drenched in the sweat of the
    night, my mind swimming in the rivers of frenetic dreams that come unbidden
    to the weary traveler, and I knew, I knew, that I must step into the cold,
    cold waters of the mountain stream that wound its way through the heart of
    the great green forest like a silver serpent, a sinuous spine of chilling
    clarity, and I tell you, my friend, I tell you that the moment I stepped
    into those waters, the moment my skin was pierced by the icy needles of that
    divine liquid, my soul was washed clean of the haze of doubt and fear, and I
    stood, reborn, as the dawn of a new day painted the sky in the colors of the
    universe.

    And so I write to you, dear friend, to tell you that you too must seek the
    salvation of the cold shower, for in the frigid embrace of the water's
    touch, there lies the key to the doors of perception, the doors that lead to
    a realm of boundless energy and endless vitality, where the mind is
    sharpened like the edge of a great warrior's blade, and the body is tempered
    like the steel of an ancient blacksmith's forge. For when you step into the
    cold, you will find that your spirit soars like a great bird of prey, your
    thoughts soaring on the wings of the eagle, the falcon, the hawk, sweeping
    through the vast and boundless skies of inspiration, creativity, and
    purpose. And you will know, as I have come to know, that the cold shower is
    the great purifier, the great invigorator, the great liberator of the soul
    from the chains of languor and indolence that bind us to the mundane and
    weary trappings of this world.

    So I implore you, dear friend, to heed my words, for they are the words of
    one who has walked the path of fire and ice, one who has danced in the
    eternal flame of the sun and bathed in the frozen tears of the moon, and I
    tell you that the way of the cold shower is the way of the enlightened, the
    way of the awakened, the way of the pioneers of the spirit who seek to
    travel beyond the boundaries of the known and into the realms of the
    infinite. And as you stand, shivering and shaking, beneath the torrent of
    the icy cascade, remember that the cold is the crucible in which the soul is
    forged, the anvil upon which the hammer of life strikes the sparks of the
    divine, and in the cold, you will find the fire, the fire that burns away
    the dross and leaves only the pure and shining gold of the spirit.

    In the cold, you will find the truth, and in the truth, you will find the
    freedom that you have sought for so long.

    Yours in the spirit of the eternal journey,

    Peter
  example_title: cold showers
parameters:
  max_length: 64
  min_length: 2
  no_repeat_ngram_size: 2
  early_stopping: true
  repetition_penalty: 4.5
  length_penalty: 0.8
  num_beams: 4
datasets:
- pszemraj/scientific_lay_summarisation-plos-norm
language:
- en
pipeline_tag: text2text-generation
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# karnold-walmer-base-biopapers

Karnold-Walmer is a text2text model based on [google/long-t5-tglobal-base](https://huggingface.co/google/long-t5-tglobal-base), specifically designed to decode the 'keywords' column of `pszemraj/scientific_lay_summarisation-plos-norm`.

Karnold-Walmer focuses on extracting relevant keywords from the input text, making it a powerful tool for keyword identification and text classification. It was fine-tuned on & supports text input of up to 16,384 tokens.

It achieves the following results on the evaluation set:
- Loss: 0.8844
- Rouge1: 46.7593
- Rouge2: 28.3538
- Rougel: 42.2921
- Rougelsum: 42.2774
- Gen Len: 78.1706



## Intended Uses & Limitations

Karnold-Walmer is intended to be used for keyword extraction and text classification in various domains, such as scientific literature, biomedical research articles, and more. By analyzing the content of an input text, the model generates a list of relevant keywords that describe the topic of the article.

It is important to note, however, that Karnold-Walmer is **specifically trained to decode text similar to the "keywords" column and is not designed for summarization tasks.** For accurate keyword extraction and text classification, the model should be used within the limits of its training data and intended purpose (see what happens when you try the out-of-domain API examples).

## Training and Evaluation Data

Karnold-Walmer was trained on the PLOS dataset, which contains full biomedical research articles paired with expert-written lay summaries and keyword lists. The model was tuned to decode the "keywords" column in the dataset, focusing on keyword extraction and text classification tasks.

### Wordcloud 

![wordcloud-kw](https://i.imgur.com/SfbAsVE.png)

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0004
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 8
- 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: 0.01
- num_epochs: 2.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len  |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| 2.0471        | 0.15  | 100  | 1.6138          | 12.4374 | 4.1861  | 11.1863 | 11.1833   | 324.6971 |
| 1.5654        | 0.3   | 200  | 1.3447          | 23.9982 | 11.1431 | 21.4173 | 21.4413   | 176.0294 |
| 1.3467        | 0.45  | 300  | 1.2038          | 33.8084 | 18.1588 | 30.4748 | 30.4142   | 107.7735 |
| 1.4398        | 0.6   | 400  | 1.1054          | 37.772  | 20.8967 | 33.859  | 33.8324   | 102.9029 |
| 1.306         | 0.75  | 500  | 1.0478          | 39.2642 | 22.0388 | 35.6578 | 35.5773   | 91.1235  |
| 1.1677        | 0.9   | 600  | 0.9994          | 40.5149 | 22.8507 | 36.3888 | 36.3499   | 103.9118 |
| 1.078         | 1.05  | 700  | 0.9627          | 42.301  | 24.2523 | 38.0739 | 38.0532   | 88.4941  |
| 1.0942        | 1.2   | 800  | 0.9443          | 44.5907 | 26.2046 | 39.7461 | 39.6763   | 88.7559  |
| 1.0209        | 1.35  | 900  | 0.9108          | 45.357  | 26.861  | 40.6411 | 40.706    | 90.1206  |
| 1.1161        | 1.5   | 1000 | 0.9026          | 47.1362 | 28.6605 | 42.6406 | 42.6108   | 79.2412  |
| 1.1224        | 1.65  | 1100 | 0.8907          | 47.31   | 28.4395 | 42.6658 | 42.6509   | 78.4265  |
| 0.9857        | 1.8   | 1200 | 0.8862          | 46.7061 | 28.1586 | 42.3181 | 42.3105   | 80.5059  |
| 1.0011        | 1.95  | 1300 | 0.8844          | 46.7593 | 28.3538 | 42.2921 | 42.2774   | 78.1706  |