autoevaluator
HF staff
Add verifyToken field to verify evaluation results are produced by Hugging Face's automatic model evaluator
eb755ed
license: | |
- apache-2.0 | |
- bsd-3-clause | |
tags: | |
- summarization | |
- summary | |
- booksum | |
- long-document | |
- long-form | |
datasets: | |
- kmfoda/booksum | |
metrics: | |
- rouge | |
languages: en | |
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 | |
parameters: | |
max_length: 48 | |
min_length: 2 | |
no_repeat_ngram_size: 3 | |
encoder_no_repeat_ngram_size: 3 | |
early_stopping: true | |
length_penalty: 0.1 | |
num_beams: 2 | |
model-index: | |
- name: pszemraj/pegasus-x-large-book-summary | |
results: | |
- task: | |
type: summarization | |
name: Summarization | |
dataset: | |
name: samsum | |
type: samsum | |
config: samsum | |
split: test | |
metrics: | |
- type: rouge | |
value: 33.1401 | |
name: ROUGE-1 | |
verified: true | |
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYjQ1NjY1OGVjYWEwMzBjMzk3ZmMyZDA0ZTcxOTdmZTUxNTc0OGYxYmY3MzJkMzFmYTVjNzU2ZTk4MzE0NWMzMSIsInZlcnNpb24iOjF9.PSHB6DMF6tkwSw5nsFE57a2ApRAy_tkS6ziKA6PSTWddEdaqfca4pfig6_olmRmcS4KxN6HHcsmioHzv4LJQBw | |
- type: rouge | |
value: 9.3095 | |
name: ROUGE-2 | |
verified: true | |
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzk3MTA3NmY1OGE3MzFjZTJhYWYzNGU4NTUzMTgwM2Y1NWZjMmEyNDNmNmEzYmQzZThjOGExMjc2ZjAyZjMzZCIsInZlcnNpb24iOjF9.tfgp8p-WlkVrfducTSg4zs-byeZMCmdZw1aizPQHXm_qRAwGtKcuVkZcmza5Y3o3VqsAEmGzg5HQD1vnZvWIDA | |
- type: rouge | |
value: 24.8552 | |
name: ROUGE-L | |
verified: true | |
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTVmMTIwNDQwNTI4MmI2MmY1ODc1Mjk0NGQ5ZWE4ZTYzOGNkMjY2ZmJhMjg2MTZlNTdhYTA2ZDAxNTFjMjA2MSIsInZlcnNpb24iOjF9.9HLgy9842oIDm6ABb3L94R1P4zAqTI0QN8aP62xzIyDxUXTbWw68PEDufYLiBJbTgZ8ElopZ9I7aou2zCgXeAA | |
- type: rouge | |
value: 29.0391 | |
name: ROUGE-LSUM | |
verified: true | |
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMmNhYWJjYjdjMzMxMmE4ZTE4NGEzMDdmZDZjODI5ZWRjZWJmYTEyZGIzYWQ2NjM3YzQ4MjI4ZTM4MmU5MzRjZSIsInZlcnNpb24iOjF9.d2yoVdmxjVJnsgIYFiLuaBO5Krgw4Axl5yeOSTKrvHygrAxoqT1nl4anzQiyoR3PwYBXwBkwmgpJUfZ7RNXtDQ | |
- type: loss | |
value: 2.288182497024536 | |
name: loss | |
verified: true | |
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYzM5NGIwODMxOTA3MTY3ODc2ZDczYTNmMTMwM2QyZmNlZjFmZDJjMGY3NWNkMDEyYzA4OTA2ZDRiODY3Zjg4OCIsInZlcnNpb24iOjF9.8k9mC050OS7mQSR9oA8liDRDQvEx1VxmTXGLmDYJVYYtTh2HYJFGP8Vy_krocFRIYDxh-IHPEOOSr5NrLMWHBA | |
- type: gen_len | |
value: 45.2173 | |
name: gen_len | |
verified: true | |
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNWZhNzQ5OTQ5Yjg5YjhlOTZiZmJhZjZiODNmY2E2OTg4YTg4NWVhYzRkNzM2Mzk4NzdlMDgxM2M4NjY2YzhhYSIsInZlcnNpb24iOjF9.tDEEsPUclZDygAdGhNrBGrF24vR8ao08Nw7hmtUt5lmSZZZK_u-8rpz97QgVS6MCJdjFVnbYC4bkFnlQWI_FAA | |
- task: | |
type: summarization | |
name: Summarization | |
dataset: | |
name: launch/gov_report | |
type: launch/gov_report | |
config: plain_text | |
split: test | |
metrics: | |
- type: rouge | |
value: 39.7279 | |
name: ROUGE-1 | |
verified: true | |
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTAxODk3OTUwMTIzODU3NzU2YzAzZjE2NTM3MzBjNDA0ZWRmZGU3NWUzNTg1YThhNDQ1NjQ5ZmM3OWI2YzBhNSIsInZlcnNpb24iOjF9.vnNKucBNt2-nIyODj9P2HeaWPX5AQR8L-DL8QzrO7kj58-vZnjT6hsAGmepRNzdZ1TLF-3j2J2plcNJ8lUO8Dg | |
- type: rouge | |
value: 10.8944 | |
name: ROUGE-2 | |
verified: true | |
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNjYzMmIxOTJmZjkxOGI5N2U0NTRmMmQwOGJhMzMxYWIzMWMzYzUwMDEyMDdiZDQ2YTUzOWU0OTViMTI2YTAwYiIsInZlcnNpb24iOjF9.De0PaAikWqfWpoIXTCYP-mSFu3PUATLX08Qq74OHXM8784heFVDX1E1sXlh_QbbKJbuMuZtTKM4qr7oLUizOAw | |
- type: rouge | |
value: 19.7018 | |
name: ROUGE-L | |
verified: true | |
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYzI3MjQzOGQ3MGE3NDNkZTEyMWRkYjUyYTYzNDEwOWVjMGFmNTBiZjE4ZTBhMGYzMmI1Yzk0YjBmYmIzMWMxZSIsInZlcnNpb24iOjF9.FVikJ5Ma0gUgM-tpbomWXnC4jtmvhxqikPqCk84t4IbIdU0CIYGTQEONiz-VqI0fJeNrnTS6lxpBv7XxKoq3BQ | |
- type: rouge | |
value: 36.5634 | |
name: ROUGE-LSUM | |
verified: true | |
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTI2OTVmNDZiZWE5ZjNkODIwZjJiNTU2ZjJjYjczODUwM2JiNDEzYmE3N2U5YWM5NzJjOWEzMmYzZjdlYWJmYyIsInZlcnNpb24iOjF9.poR4zcqRvdaierfWFdTa53Cv6ZbNbnRwyRTi9HukHF5AWAQgc6zpBLkwOYFYoWjuSH83ohWeMM3MoIdw3zypBw | |
- type: loss | |
value: 2.473011016845703 | |
name: loss | |
verified: true | |
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDFmMjg3NWQ2YTMxMTc1OGZiYWYzNjg5NDY3MWE4MjY5ZDQxZDZhZGI1OTc5MzZkZGEzYmVlNWFiMzZjNDdhNCIsInZlcnNpb24iOjF9.05nKB3SmEfFKSduJqlleF4Fd2_IhwJS8eTOrnzZYCQQfLCfpJAZLhp3eLQCuBY4htd-FNrZftrThL66zVxyrCQ | |
- type: gen_len | |
value: 212.8243 | |
name: gen_len | |
verified: true | |
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOGNjMTg4ZDZlZjAxZGNhN2M0NWI0ZTA0OWEzNDkzNDAzOTJhODA2MmVkODI4YjYzN2FiOTU1ZDMwM2VlNWMyYyIsInZlcnNpb24iOjF9.WYx6XJFKokY2heoN-jpAMp1Z1gsyJus3zpktQgNd0FOYJxOUqW40A0kkHtd15y4dUhsbccLpuJGY1fNJgHOiDw | |
- task: | |
type: summarization | |
name: Summarization | |
dataset: | |
name: billsum | |
type: billsum | |
config: default | |
split: test | |
metrics: | |
- type: rouge | |
value: 42.1065 | |
name: ROUGE-1 | |
verified: true | |
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZDJhNDM2MWEwMjJlYjRmZTVkYzljODcwMzlmMGUxMDA4ZmRjNjM0NmY3ZWJlMmZjNGI3NDQ3NTQyOTQ3MjBkNSIsInZlcnNpb24iOjF9.l1MiZbXyFyXAcsfFChMrTvSaBhzBR6AuDnBuII8zY3Csz3ShWK0vo09MkQdZ1epe8PKWV9wwUBuJyKk3wL7MDw | |
- type: rouge | |
value: 15.4079 | |
name: ROUGE-2 | |
verified: true | |
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTY3NDBkYTVkNjdhY2I0ZmY0NTA4YzVkMGE5YWE5ODdjOGE1MDhkOTJhOWY3NmI2ZWI1MGU2MGI1NDRlYjI3MSIsInZlcnNpb24iOjF9.VN-5eK2SzFDCJnFTHHu7XCU_lynaxW_JEDc3llmcNo_ffDgRmISHHGaqV7fPFymBBMXpPly7XblO_sukyqj1Cg | |
- type: rouge | |
value: 24.8814 | |
name: ROUGE-L | |
verified: true | |
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZDYyNGZmNDY3MTY4YzI4ZjZhODE0NGIyN2ZkOGEyYzM3MWZjM2QzZTg5ZjNmZmYzZDE5NzhiZDQ4OGM1YjNiMyIsInZlcnNpb24iOjF9.L73M1M5XdMQkf8zSdfLN0MUrxtO0r6UiLjoOkHfrIGbWNsNJ8tU5lciYFNIhJrICUL8LchCsFqR9LAClKS4bCg | |
- type: rouge | |
value: 36.0375 | |
name: ROUGE-LSUM | |
verified: true | |
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTBlMTQ5OTQxNTA3ZmFiMGYyZWQ0MGM0ODY2YWI3MzgyNjkwNzQyM2FmNGRjMzc3MjJmZDZkOWY4M2RhZTg2MSIsInZlcnNpb24iOjF9.IiMSSVahBgH8n34bGCC_DDGpujDXQbIvGhlcpVV2EBVQLLWUqcCy5WwBdbRrxPC-asBRCNERQxj8Uii4FvPsDQ | |
- type: loss | |
value: 1.9130958318710327 | |
name: loss | |
verified: true | |
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTg2NTMxZDE3MDg3MDFkMTYxNjY1OTc5YjQ4ODcyMGUxMTFiZjJiNDgyYWZhN2NjZmE1MDQ1NTRmZGY0NjQzZSIsInZlcnNpb24iOjF9.kADUBMO8i6-oGDDt1cOiGMrGcMkF_Qc1jSpS2NSFyksDRusQa_YuuShefF4DuHVEr3CS0hNjjRH9_JBeX9ZQDg | |
- type: gen_len | |
value: 179.2184 | |
name: gen_len | |
verified: true | |
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNjM4NGNiMTY3YzZjMzg4MTRiMDdiZDFiMzA1ZDIyMDM2MDk1OWRhYWQzN2UxZDNlODIxOWVhY2JlYjk4Mjk5YyIsInZlcnNpb24iOjF9.nU8ImMNWgjg9BKjUBJQLFaJOBq3kyIne8ldlpL0OV0e4888wOntIAcJP0dCCYfRSLVmZuXQ1M8cpDuTf50hNCw | |
- task: | |
type: summarization | |
name: Summarization | |
dataset: | |
name: kmfoda/booksum | |
type: kmfoda/booksum | |
config: kmfoda--booksum | |
split: test | |
metrics: | |
- type: rouge | |
value: 35.2154 | |
name: ROUGE-1 | |
verified: true | |
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- task: | |
type: summarization | |
name: Summarization | |
dataset: | |
name: big_patent | |
type: big_patent | |
config: y | |
split: test | |
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# pszemraj/pegasus-x-large-book-summary | |
<a href="https://colab.research.google.com/gist/pszemraj/6c326c0649233ab017d63adc36958d1a/pegasus-x-large-booksum-demo.ipynb"> | |
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> | |
</a> | |
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. | |
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 eight epochs. | |
## Model description | |
More information needed | |
## Intended uses & limitations | |
- This seems to be the GPU-hungriest summarization model yet. | |
## Training and evaluation data | |
More information needed | |
## Training procedure | |
### Training hyperparameters | |
#### Epochs 1-4 | |
TODO | |
#### Epochs 5 & 6 | |
The following hyperparameters were used during training: | |
- learning_rate: 6e-05 | |
- train_batch_size: 4 | |
- eval_batch_size: 1 | |
- seed: 42 | |
- distributed_type: multi-GPU | |
- gradient_accumulation_steps: 32 | |
- total_train_batch_size: 128 | |
- optimizer: _ADAN_ using lucidrains' `adan-pytorch` with default betas | |
- lr_scheduler_type: constant_with_warmup | |
- data type: TF32 | |
- num_epochs: 2 | |
#### Epochs 7 & 8 | |
- epochs 5 & 6 were trained with 12288 tokens input | |
- this fixes that with 2 epochs at 16384 tokens input | |
The following hyperparameters were used during training: | |
- learning_rate: 0.0004 | |
- train_batch_size: 4 | |
- eval_batch_size: 1 | |
- seed: 42 | |
- distributed_type: multi-GPU | |
- gradient_accumulation_steps: 16 | |
- total_train_batch_size: 64 | |
- optimizer: _ADAN_ using lucidrains' `adan-pytorch` with default betas | |
- lr_scheduler_type: cosine | |
- lr_scheduler_warmup_ratio: 0.03 | |
- num_epochs: 2 | |
### Framework versions | |
- Transformers 4.22.0 | |
- Pytorch 1.11.0a0+17540c5 | |
- Datasets 2.4.0 | |
- Tokenizers 0.12.1 | |