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README.md

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+---
+language:
+- en
+license: apache-2.0
+library_name: transformers
+tags:
+- lay summaries
+- paper summaries
+- biology
+- medical
+datasets:
+- pszemraj/scientific_lay_summarisation-plos-norm
+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
+pipeline_tag: summarization
+base_model: google/long-t5-tglobal-base
+---
+
+# long-t5-tglobal-base-sci-simplify
+
+<a href="https://colab.research.google.com/gist/pszemraj/f0dc02c4d4a5c7ad1d5bf3953251145d/long-t5-tglobal-base-sci-simplify-plos-example-with-textsum.ipynb">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
+</a>
+
+Exploring how well long-document models trained on "lay summaries" of scientific papers generalize. 
+
+> A lay summary is a summary of a research paper or scientific study that is written in plain language, without the use of technical jargon, and is designed to be easily understood by non-experts.
+
+## Model description
+
+This model is a fine-tuned version of [google/long-t5-tglobal-base](https://huggingface.co/google/long-t5-tglobal-base) on the `pszemraj/scientific_lay_summarisation-plos-norm` dataset for two epochs.
+
+- The variant trained on the ELIFE subset can be found [here](https://huggingface.co/pszemraj/long-t5-tglobal-base-sci-simplify-elife)
+
+## Usage 
+
+It's recommended to use this model with [beam search decoding](https://huggingface.co/docs/transformers/generation_strategies#beamsearch-decoding). If you are interested, you can also use the `textsum` util repo to have most of this abstracted for you:
+
+
+Install with `pip`:
+
+```bash
+pip install -U textsum
+```
+
+Use in python:
+
+```python
+from textsum.summarize import Summarizer
+
+summarizer = Summarizer('pszemraj/long-t5-tglobal-base-sci-simplify')
+text = "put the text you don't want to read here"
+summary = summarizer.summarize_string(text)
+print(summary)
+```
+
+## Intended uses & limitations
+
+- Ability to generalize outside of the dataset domain (pubmed/bioscience type papers) has to be evaluated.
+
+
+## Training procedure
+
+
+### Eval results
+
+It achieves the following results on the evaluation set:
+- Loss: 1.6778
+- Rouge1: 49.1475
+- Rouge2: 18.9281
+- Rougel: 26.9893
+- Rougelsum: 45.0973
+- Gen Len: 399.4125
+
+### 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: 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: 0.01
+- num_epochs: 2.0
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len  |
+|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
+| 1.966         | 0.52  | 200  | 1.7171          | 48.6521 | 18.427  | 26.7726 | 44.3947   | 376.335  |
+| 1.877         | 1.03  | 400  | 1.6909          | 49.3263 | 18.7945 | 27.0741 | 45.1737   | 382.205  |
+| 1.9007        | 1.55  | 600  | 1.6778          | 49.1475 | 18.9281 | 26.9893 | 45.0973   | 399.4125 |

config.json

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+  ],
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+  "d_kv": 64,
+  "d_model": 768,
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+  "torch_dtype": "float32",
+  "transformers_version": "4.27.4",
+  "use_cache": true,
+  "vocab_size": 32128
+}

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