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--- |
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languages: en |
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license: |
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- apache-2.0 |
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- bsd-3-clause |
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datasets: |
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- kmfoda/booksum |
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tags: |
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- summarization |
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- summary |
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- booksum |
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- long-document |
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- long-form |
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metrics: |
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- rouge |
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widget: |
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- text: large earthquakes along a given fault segment do not occur at random intervals |
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because it takes time to accumulate the strain energy for the rupture. The rates |
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at which tectonic plates move and accumulate strain at their boundaries are approximately |
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uniform. Therefore, in first approximation, one may expect that large ruptures |
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of the same fault segment will occur at approximately constant time intervals. |
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If subsequent main shocks have different amounts of slip across the fault, then |
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the recurrence time may vary, and the basic idea of periodic mainshocks must be |
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modified. For great plate boundary ruptures the length and slip often vary by |
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a factor of 2. Along the southern segment of the San Andreas fault the recurrence |
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interval is 145 years with variations of several decades. The smaller the standard |
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deviation of the average recurrence interval, the more specific could be the long |
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term prediction of a future mainshock. |
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example_title: earthquakes |
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- text: " A typical feed-forward neural field algorithm. Spatiotemporal coordinates\ |
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\ are fed into a neural network that predicts values in the reconstructed domain.\ |
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\ Then, this domain is mapped to the sensor domain where sensor measurements are\ |
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\ available as supervision. Class and Section Problems Addressed Generalization\ |
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\ (Section 2) Inverse problems, ill-posed problems, editability; symmetries. Hybrid\ |
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\ Representations (Section 3) Computation & memory efficiency, representation\ |
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\ capacity, editability: Forward Maps (Section 4) Inverse problems Network Architecture\ |
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\ (Section 5) Spectral bias, integration & derivatives. Manipulating Neural Fields\ |
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\ (Section 6) Edit ability, constraints, regularization. Table 2: The five classes\ |
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\ of techniques in the neural field toolbox each addresses problems that arise\ |
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\ in learning, inference, and control. (Section 3). We can supervise reconstruction\ |
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\ via differentiable forward maps that transform Or project our domain (e.g, 3D\ |
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\ reconstruction via 2D images; Section 4) With appropriate network architecture\ |
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\ choices, we can overcome neural network spectral biases (blurriness) and efficiently\ |
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\ compute derivatives and integrals (Section 5). Finally, we can manipulate neural\ |
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\ fields to add constraints and regularizations, and to achieve editable representations\ |
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\ (Section 6). Collectively, these classes constitute a 'toolbox' of techniques\ |
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\ to help solve problems with neural fields There are three components in a conditional\ |
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\ neural field: (1) An encoder or inference function \u20AC that outputs the conditioning\ |
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\ latent variable 2 given an observation 0 E(0) =2. 2 is typically a low-dimensional\ |
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\ vector, and is often referred to aS a latent code Or feature code_ (2) A mapping\ |
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\ function 4 between Z and neural field parameters O: Y(z) = O; (3) The neural\ |
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\ field itself $. The encoder \u20AC finds the most probable z given the observations\ |
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\ O: argmaxz P(2/0). The decoder maximizes the inverse conditional probability\ |
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\ to find the most probable 0 given Z: arg- max P(Olz). We discuss different encoding\ |
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\ schemes with different optimality guarantees (Section 2.1.1), both global and\ |
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\ local conditioning (Section 2.1.2), and different mapping functions Y (Section\ |
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\ 2.1.3) 2. Generalization Suppose we wish to estimate a plausible 3D surface\ |
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\ shape given a partial or noisy point cloud. We need a suitable prior over the\ |
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\ sur- face in its reconstruction domain to generalize to the partial observations.\ |
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\ A neural network expresses a prior via the function space of its architecture\ |
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\ and parameters 0, and generalization is influenced by the inductive bias of\ |
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\ this function space (Section 5)." |
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example_title: scientific paper |
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- text: 'Is a else or outside the cob and tree written being of early client rope |
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and you have is for good reasons. On to the ocean in Orange for time. By''s the |
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aggregate we can bed it yet. Why this please pick up on a sort is do and also |
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M Getoi''s nerocos and do rain become you to let so is his brother is made in |
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use and Mjulia''s''s the lay major is aging Masastup coin present sea only of |
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Oosii rooms set to you We do er do we easy this private oliiishs lonthen might |
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be okay. Good afternoon everybody. Welcome to this lecture of Computational Statistics. |
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As you can see, I''m not socially my name is Michael Zelinger. I''m one of the |
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task for this class and you might have already seen me in the first lecture where |
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I made a quick appearance. I''m also going to give the tortillas in the last third |
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of this course. So to give you a little bit about me, I''m a old student here |
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with better Bulman and my research centres on casual inference applied to biomedical |
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disasters, so that could be genomics or that could be hospital data. If any of |
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you is interested in writing a bachelor thesis, a semester paper may be mastathesis |
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about this topic feel for reach out to me. you have my name on models and my email |
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address you can find in the directory I''d Be very happy to talk about it. you |
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do not need to be sure about it, we can just have a chat. So with that said, let''s |
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get on with the lecture. There''s an exciting topic today I''m going to start |
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by sharing some slides with you and later on during the lecture we''ll move to |
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the paper. So bear with me for a few seconds. Well, the projector is starting |
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up. Okay, so let''s get started. Today''s topic is a very important one. It''s |
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about a technique which really forms one of the fundamentals of data science, |
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machine learning, and any sort of modern statistics. It''s called cross validation. |
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I know you really want to understand this topic I Want you to understand this |
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and frankly, nobody''s gonna leave Professor Mineshousen''s class without understanding |
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cross validation. So to set the stage for this, I Want to introduce you to the |
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validation problem in computational statistics. So the problem is the following: |
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You trained a model on available data. You fitted your model, but you know the |
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training data you got could always have been different and some data from the |
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environment. Maybe it''s a random process. You do not really know what it is, |
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but you know that somebody else who gets a different batch of data from the same |
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environment they would get slightly different training data and you do not care |
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that your method performs as well. On this training data. you want to to perform |
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well on other data that you have not seen other data from the same environment. |
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So in other words, the validation problem is you want to quantify the performance |
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of your model on data that you have not seen. So how is this even possible? How |
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could you possibly measure the performance on data that you do not know The solution |
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to? This is the following realization is that given that you have a bunch of data, |
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you were in charge. You get to control how much that your model sees. It works |
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in the following way: You can hide data firms model. Let''s say you have a training |
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data set which is a bunch of doubtless so X eyes are the features those are typically |
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hide and national vector. It''s got more than one dimension for sure. And the |
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why why eyes. Those are the labels for supervised learning. As you''ve seen before, |
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it''s the same set up as we have in regression. And so you have this training |
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data and now you choose that you only use some of those data to fit your model. |
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You''re not going to use everything, you only use some of it the other part you |
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hide from your model. And then you can use this hidden data to do validation from |
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the point of you of your model. This hidden data is complete by unseen. In other |
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words, we solve our problem of validation.' |
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example_title: transcribed audio - lecture |
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- text: "Transformer-based models have shown to be very useful for many NLP tasks.\ |
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\ However, a major limitation of transformers-based models is its O(n^2)O(n 2)\ |
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\ time & memory complexity (where nn is sequence length). Hence, it's computationally\ |
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\ very expensive to apply transformer-based models on long sequences n > 512n>512.\ |
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\ Several recent papers, e.g. Longformer, Performer, Reformer, Clustered attention\ |
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\ try to remedy this problem by approximating the full attention matrix. You can\ |
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\ checkout \U0001F917's recent blog post in case you are unfamiliar with these\ |
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\ models.\nBigBird (introduced in paper) is one of such recent models to address\ |
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\ this issue. BigBird relies on block sparse attention instead of normal attention\ |
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\ (i.e. BERT's attention) and can handle sequences up to a length of 4096 at a\ |
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\ much lower computational cost compared to BERT. It has achieved SOTA on various\ |
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\ tasks involving very long sequences such as long documents summarization, question-answering\ |
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\ with long contexts.\nBigBird RoBERTa-like model is now available in \U0001F917\ |
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Transformers. The goal of this post is to give the reader an in-depth understanding\ |
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\ of big bird implementation & ease one's life in using BigBird with \U0001F917\ |
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Transformers. But, before going into more depth, it is important to remember that\ |
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\ the BigBird's attention is an approximation of BERT's full attention and therefore\ |
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\ does not strive to be better than BERT's full attention, but rather to be more\ |
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\ efficient. It simply allows to apply transformer-based models to much longer\ |
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\ sequences since BERT's quadratic memory requirement quickly becomes unbearable.\ |
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\ Simply put, if we would have \u221E compute & \u221E time, BERT's attention\ |
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\ would be preferred over block sparse attention (which we are going to discuss\ |
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\ in this post).\nIf you wonder why we need more compute when working with longer\ |
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\ sequences, this blog post is just right for you!\nSome of the main questions\ |
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\ one might have when working with standard BERT-like attention include:\nDo all\ |
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\ tokens really have to attend to all other tokens? Why not compute attention\ |
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\ only over important tokens? How to decide what tokens are important? How to\ |
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\ attend to just a few tokens in a very efficient way? In this blog post, we will\ |
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\ try to answer those questions.\nWhat tokens should be attended to? We will give\ |
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\ a practical example of how attention works by considering the sentence 'BigBird\ |
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\ is now available in HuggingFace for extractive question answering'. In BERT-like\ |
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\ attention, every word would simply attend to all other tokens.\nLet's think\ |
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\ about a sensible choice of key tokens that a queried token actually only should\ |
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\ attend to by writing some pseudo-code. Will will assume that the token available\ |
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\ is queried and build a sensible list of key tokens to attend to.\n>>> # let's\ |
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\ consider following sentence as an example >>> example = ['BigBird', 'is', 'now',\ |
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\ 'available', 'in', 'HuggingFace', 'for', 'extractive', 'question', 'answering']\n\ |
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>>> # further let's assume, we're trying to understand the representation of 'available'\ |
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\ i.e. >>> query_token = 'available' >>> # We will initialize an empty `set` and\ |
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\ fill up the tokens of our interest as we proceed in this section. >>> key_tokens\ |
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\ = [] # => currently 'available' token doesn't have anything to attend Nearby\ |
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\ tokens should be important because, in a sentence (sequence of words), the current\ |
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\ word is highly dependent on neighboring past & future tokens. This intuition\ |
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\ is the idea behind the concept of sliding attention." |
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example_title: bigbird blog intro |
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- text: "To be fair, you have to have a very high IQ to understand Rick and Morty.\ |
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\ The humour is extremely subtle, and without a solid grasp of theoretical physics\ |
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\ most of the jokes will go over a typical viewer's head. There's also Rick's\ |
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\ nihilistic outlook, which is deftly woven into his characterisation- his personal\ |
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\ philosophy draws heavily from Narodnaya Volya literature, for instance. The\ |
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\ fans understand this stuff; they have the intellectual capacity to truly appreciate\ |
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\ the depths of these jokes, to realise that they're not just funny- they say\ |
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\ something deep about LIFE. As a consequence people who dislike Rick & Morty\ |
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\ truly ARE idiots- of course they wouldn't appreciate, for instance, the humour\ |
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\ in Rick's existential catchphrase 'Wubba Lubba Dub Dub,' which itself is a cryptic\ |
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\ reference to Turgenev's Russian epic Fathers and Sons. I'm smirking right now\ |
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\ just imagining one of those addlepated simpletons scratching their heads in\ |
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\ confusion as Dan Harmon's genius wit unfolds itself on their television screens.\ |
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\ What fools.. how I pity them. \U0001F602\nAnd yes, by the way, i DO have a Rick\ |
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\ & Morty tattoo. And no, you cannot see it. It's for the ladies' eyes only- and\ |
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\ even then they have to demonstrate that they're within 5 IQ points of my own\ |
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\ (preferably lower) beforehand. Nothin personnel kid \U0001F60E" |
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example_title: Richard & Mortimer |
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parameters: |
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max_length: 64 |
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min_length: 4 |
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no_repeat_ngram_size: 3 |
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early_stopping: true |
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length_penalty: 0.3 |
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repetition_penalty: 3.5 |
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encoder_no_repeat_ngram_size: 3 |
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num_beams: 1 |
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--- |
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# pszemraj/pegasus-x-large-book-summary |
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[](https://colab.research.google.com/gist/pszemraj/6c326c0649233ab017d63adc36958d1a/pegasus-x-large-booksum-demo.ipynb) |
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Get SparkNotes-esque summaries of arbitrary text! Due to the model size it's recommended to try it out in Colab (linked above) as the API textbox may time out. |
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This model is a fine-tuned version of [google/pegasus-x-large](https://huggingface.co/google/pegasus-x-large) on the `kmfoda/booksum` dataset for approx six epochs. |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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#### Epochs 1-4 |
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TODO |
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#### Epochs 5 & 6 |
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The following hyperparameters were used during training: |
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- learning_rate: 6e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- gradient_accumulation_steps: 32 |
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- total_train_batch_size: 128 |
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- optimizer: _ADAN_ using lucidrains' `adan-pytorch` with default betas |
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- lr_scheduler_type: constant_with_warmup |
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- data type: TF32 |
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- num_epochs: 2 |
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#### Epochs 7 & 8 |
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- epochs 5 & 6 were trained with 12288 tokens input |
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- this fixes that with 2 epochs at 16384 tokens input |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0004 |
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- train_batch_size: 4 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.03 |
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- num_epochs: 2 |
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### Framework versions |
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- Transformers 4.22.0 |
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- Pytorch 1.11.0a0+17540c5 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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