Long T5 Tglobal Base 16384 Book Summary

🚀 Summarization Model

This project focuses on summarization tasks, offering solutions for long - document and long - form text summarization. It utilizes relevant datasets and metrics to ensure high - quality results.

✨ Features

• Task - Oriented: Specialized for summarization, including book summaries.
• Dataset - Based: Trained on the kmfoda/booksum dataset.
• Metric - Driven: Evaluated using the ROUGE metric.

📄 License

This project is licensed under the following licenses:

• Apache - 2.0
• BSD - 3 - Clause

💻 Usage Examples

Basic Usage

You can use the model for summarization tasks. Here are some examples:

Example 1: Earthquakes

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 2: Scientific Paper

 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 3: Transcribed Audio - Lecture

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 4: BigBird Blog Intro

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 5: Rick and Morty

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 imaginin