Update README.md

README.md

@@ -1,18 +1,189 @@
 ---
-license: bsd-3-clause
-tags:
-- generated_from_trainer
-model-index:
-- name: model-checkpoints
-  results: []
+languages: en
+license:
+- apache-2.0
+- bsd-3-clause
+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 \u20AC 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 \u20AC 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 \U0001F917's recent blog post in case you are unfamiliar with these\
+    \ models.\nBigBird (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.\nBigBird RoBERTa-like model is now available in \U0001F917\
+    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 \U0001F917\
+    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 \u221E compute & \u221E time, BERT's attention\
+    \ would be preferred over block sparse attention (which we are going to discuss\
+    \ in this post).\nIf you wonder why we need more compute when working with longer\
+    \ sequences, this blog post is just right for you!\nSome of the main questions\
+    \ one might have when working with standard BERT-like attention include:\nDo 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.\nWhat 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.\nLet'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.\n>>> # let's\
+    \ consider following sentence as an example >>> example = ['BigBird', 'is', 'now',\
+    \ 'available', 'in', 'HuggingFace', 'for', 'extractive', 'question', 'answering']\n\
+    >>> # 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. \U0001F602\nAnd 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 \U0001F60E"
+  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
 ---
 
-<!-- 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. -->
+# pszemraj/pegasus-x-large-book-summary
 
-# model-checkpoints
+ [![colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/gist/pszemraj/fbc04a81a305b3f98ee0855835fef9aa/pegasus-x-large-booksum-demo.ipynb)
 
-This model is a fine-tuned version of [pszemraj/pegasus-x-large-booksum-WIP2](https://huggingface.co/pszemraj/pegasus-x-large-booksum-WIP2) on the kmfoda/booksum dataset.
+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.
 
 ## Model description
 
@@ -30,6 +201,11 @@ More information needed
 
 ### Training hyperparameters
 
+#### Epochs 1-4
+
+TODO
+
+#### Epochs 5 & 6
 The following hyperparameters were used during training:
 - learning_rate: 6e-05
 - train_batch_size: 4