|
--- |
|
license: |
|
- apache-2.0 |
|
- bsd-3-clause |
|
tags: |
|
- summarization |
|
- summary |
|
- booksum |
|
- long-document |
|
- long-form |
|
datasets: |
|
- kmfoda/booksum |
|
- big_patent |
|
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 € 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: 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 |
|
model-index: |
|
- name: pszemraj/long-t5-tglobal-base-16384-booksum-V11-big_patent-V2 |
|
results: |
|
- task: |
|
type: summarization |
|
name: Summarization |
|
dataset: |
|
name: kmfoda/booksum |
|
type: kmfoda/booksum |
|
config: kmfoda--booksum |
|
split: test |
|
metrics: |
|
- type: rouge |
|
value: 23.1439 |
|
name: ROUGE-1 |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZmQzMDk0MDJlZTJkN2IzODg3NDJhYmY4MzJmOTU4N2FjMDBjODg5NzJlMGFhNDQ2YTFhMzI3YmY5ZWM1MDBkMiIsInZlcnNpb24iOjF9.yoXEV5ircj_cjQhUA_RpWH_8Kaev0sRLwQulYD8wmqxfSEuqamBGedXnIg9X_EcpjvulBhapjGZN2G0s0vz4Dg |
|
- type: rouge |
|
value: 3.2393 |
|
name: ROUGE-2 |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTkwNzEwYjc5YTZkMmE4NmEwMDE1OTRiNTJmM2VlYmI3NmM2NjIwZWMxM2ZkNjU2MzhjMmQzYjIxODRiYzY4ZiIsInZlcnNpb24iOjF9.CDK_e4fCwERbm3D_Y2tc41SSscIvlZKGTUQ16afpMuH2_HHKbpn7CNgtU9MWiyFZfdgafdUeQPo2CCYI-dCBCg |
|
- type: rouge |
|
value: 12.7038 |
|
name: ROUGE-L |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDFkNjcyYmYxYzdlMTY2NTIyY2ZiZDJlZjliYTM1YWZjZGI3YzA5ZDczYjdkMGUzZmUxNmJkMDY0OTk3NWNlMSIsInZlcnNpb24iOjF9.XQmt4GEX0N6y2FNXfLAeLDkB96nJyxhN9dyy-OdBcu5E7Tw0dvIN3feYHxq8MenTShE9lsekIYZy2kieJQfmCg |
|
- type: rouge |
|
value: 19.8101 |
|
name: ROUGE-LSUM |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYTFhMGNhMzA0YmYyMDhiNzdlMDc2ZDQ3YjFjMDM3ODliMmIxMjQxZWMwYWM0NTM0OGNlZTkzMzVhZDBmMjA1YiIsInZlcnNpb24iOjF9.-YChaP7xwLM9W5jrdLSyLWdb3hAdPbm0mmij3X_pU3nqb3_wuPobjCLGEEQNxAnGq7kE-LI5hgXZ-lGhuKUCCQ |
|
- type: loss |
|
value: 2.766307830810547 |
|
name: loss |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiODAxYzRhNGM2ZGVkOWRiM2Y4NzNjZDM2MTY2MmM4MzY3ZWM5ZjdmMWUxZGY5Y2E2OTg4ZGEwYzBlMmFiYmQyNSIsInZlcnNpb24iOjF9.VRePqe8Z9dD5l6bsfIRLkFn4mwwVC8G--kOlofQWSiGusRxVrY50fa5MtKTGmuiNs5JDFCPjZmkpGYlSxnOeDw |
|
- type: gen_len |
|
value: 63.4493 |
|
name: gen_len |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZGY4NWI0MDc3NDk4NTg4YjQ5YzFmN2MyYWFjMzI0MjlkMGZlMWMzYThiMDFlMmM3MmE4ODg0YWExNTMyZjQ5MiIsInZlcnNpb24iOjF9.Ym3jfW0gthJhlLg4CW10jM9YUHUGbAPIdLefE3CTyP0OUrV9yuJAGV6-RDrV-Viwyy1Xaqg4BFa5pX7P2PRRDQ |
|
- task: |
|
type: summarization |
|
name: Summarization |
|
dataset: |
|
name: samsum |
|
type: samsum |
|
config: samsum |
|
split: test |
|
metrics: |
|
- type: rouge |
|
value: 26.8026 |
|
name: ROUGE-1 |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTBhYTQzMGVjZTJjZmE3NjBiNzI2M2FlNTA4Yzk5Njc1Yjk1YTk2NTJiMTRlMzQ3NjU2ZjQxZTNkNDVhNjMzYSIsInZlcnNpb24iOjF9.GyFUubKI3pM5Z8I1jz6Q_f7fSr1nVpwuFluUOVq8aaWfv7L1dZ_5By2FShQM1nwBM-mCiqtFb3a61eR3VEAeBw |
|
- type: rouge |
|
value: 6.0656 |
|
name: ROUGE-2 |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzEyZTYxYmVlYTc0MzNhMWM1ODgwODRiYWNkN2FjMjIzOTJhNzA0OTFkY2M0ZTJhMWMzNWMzY2E1OGJmYTg5OCIsInZlcnNpb24iOjF9.3U0PamPVFWWE7Nxh6u52mnMP-HpeGPEOLauZthcj32ELSuNx9s260ujguSW_BrJpCXqNNEqIzYTlWf97Ji8vCA |
|
- type: rouge |
|
value: 20.0098 |
|
name: ROUGE-L |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOGExYTRmZDgzYzllNWZmMGFlN2FhMDJmZGE1ODkyYTZlNmFhZjZmNGU4YzQwZGZiYTAyZmI1NGJmNjRjODkwYSIsInZlcnNpb24iOjF9.dEON7kZa7dKCHjz7nuuIBdcpwojM5-OxQuEf5n18ZywWdbk9H2LWGY2uvvCRp6cK2JsIzxzTmX9wK7zkWQiCAA |
|
- type: rouge |
|
value: 21.9115 |
|
name: ROUGE-LSUM |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiM2Y4MWE4ZmIyMTA5YWU5YzllYzExMzA1OTc2Mjg3NTYxNjcwMWMxZGI0ZDhmYjJhMGIxNTllY2Q3NDVlNmM2MiIsInZlcnNpb24iOjF9.M8bYXCuNHyVAkA4vBbqvGe8yCgmjCrlhqqliAF6WcmrYRF8CvezQ4S4SWGhhVkcG6v84H-Pa9LzsKmualXdWBw |
|
- type: loss |
|
value: 2.317471981048584 |
|
name: loss |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMmI1YjNlYzI3OTY4YjY1MDIwYzk3ZDMzZDA4MzQwM2ZhNzY3NDQxZTA2ZThiMmE2MmFmNTg0OGMyYWFhODE5OSIsInZlcnNpb24iOjF9.QpoWo_TLKw72_PbtwknBA1LbUQ8ftls-8VBLuN8_ZhUN2lNNpipU2qMZ1Ga4xAUazkcMhT_TwpqjyGshJFkgAg |
|
- type: gen_len |
|
value: 19.1111 |
|
name: gen_len |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYTA2MmFiNjI5NzFjOTUzMTEwZTNiYzA1OGY1ZWEyNTE1ZTgzYjMxNDE4YjJkZmIxNWI4MDMyYWUxMWRkODk1NCIsInZlcnNpb24iOjF9.CXy-Dfle9ypabrK3I1GyhOWl46EyRDbf8XlY-D0cNktXcCCbKdgn8DWgJI199GJpH-19mMS_jQt049VJri2EDw |
|
- task: |
|
type: summarization |
|
name: Summarization |
|
dataset: |
|
name: xsum |
|
type: xsum |
|
config: default |
|
split: test |
|
metrics: |
|
- type: rouge |
|
value: 25.2061 |
|
name: ROUGE-1 |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjZmZDRlN2NjZTQyNzkyMmZiYzk1MjJmMmE0MGM4ZjUwOGNmOGFhZjg0MzE0MzM4MmE1Y2EyYTY4ZThmNzUzMiIsInZlcnNpb24iOjF9.pdJWpUnMeqftinZrPkkFRWbCA253BYgt5W-EqbyTVi9BteojJ6yEDbMjE0TyYzlJ28JBcw4IVNL2zaWCgpfRBQ |
|
- type: rouge |
|
value: 4.7048 |
|
name: ROUGE-2 |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNGRjOGUzZTk1ZDc0Zjk5MmE4ZjUzNmZiZjQ2YzE2YzYzODdmYmY3NzMwNDdmYmViNjVkZTUzMmY4YjllOGQ1NCIsInZlcnNpb24iOjF9.nFiT7HhUZSDofK6_UH2-1rzPz_48w7e5j0Q72vqgodSNIwpv2JOlcb1GOlaA9jkvy45PJyDBgP9i6kLVfaNBBw |
|
- type: rouge |
|
value: 17.8593 |
|
name: ROUGE-L |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZmY5ZjM0ZjdkYTZiMzk0ZWYyM2EzZWNjMjczMjI2MzkwYmNiN2JhNDEzNzdmMmE0NzEwNmVkNGU5YTlkZDAzYyIsInZlcnNpb24iOjF9.C3ZgUsGNNtwZVJFcT90KkBfewrrA3ZXxxVl2u5ykUtzpS4gzoaRuZbPT8WOJAog7kfPPJiG_GZGYy9XTTCdIBw |
|
- type: rouge |
|
value: 18.0798 |
|
name: ROUGE-LSUM |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMDU4Y2Y3MzExNzNlZTI3NWVmZTNjMmZkNTAxNDBjMzJiZTI5M2E2N2ViODk5OGEwZGU5NzYxZWMzMjMwNmQ2MSIsInZlcnNpb24iOjF9.qDLZsjtftvlw8-3kOoUvanWmemmvaPxUIAxOVh1B18Ihn9kkm0FnZbWxl65YdOLg3dqDcHnDFXvXcS81C8dmBw |
|
- type: loss |
|
value: 3.003053665161133 |
|
name: loss |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTM2ODRkMjk5MjczY2ViZGVjMjJjOTFmYTk2NTAyNmUwMTRiZjYwZTllY2NhODFhYWVkZTIzYzQxZjZlOGFkNCIsInZlcnNpb24iOjF9.3SeJzRO0b4cNCTOgsf7c8UrLCLW-6JoOHtNMmMr5DCzNzfqlt2TSJ5ClahzzAYA2_5QhTMhcUYOewH5uZhkpDA |
|
- type: gen_len |
|
value: 27.4815 |
|
name: gen_len |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZDdiYTVkZGI0NzE0ODcwNjgwNGQ0YmNjZDI1MWQxZWQ0MzNmMDJkYmE4MGM5ZjM4NGViNWZiNTdjNTg2YzBlOSIsInZlcnNpb24iOjF9.VoPyoq8HZq8nbucrPYt52flRFtkD5VAfVD7LykAp-GiN2W6D3cpcagMMrHThP9e8q3qDodxddMcnwY88CGtkAg |
|
- task: |
|
type: summarization |
|
name: Summarization |
|
dataset: |
|
name: cnn_dailymail |
|
type: cnn_dailymail |
|
config: 3.0.0 |
|
split: test |
|
metrics: |
|
- type: rouge |
|
value: 27.5692 |
|
name: ROUGE-1 |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiM2UzNDRjNDJhNjViYjgxNDY2NzAwODkyYjk1OTllNWFiYmI2MGEyMmM3ZTc1YWZjNjhiZDZkYzAxYzIwYTQzZiIsInZlcnNpb24iOjF9.FEJU7de6nnYa1rhAngf3h0JDSFKXzWKkcHwQtcz6rbPuVV0Jw7u-9PwDXBFh0X8n2PJjOfCqM5hmcrUe0FxkCQ |
|
- type: rouge |
|
value: 6.1264 |
|
name: ROUGE-2 |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMGIzODA2ZjU2YmM0YmJiZDIxNzQ0ZDI1NGQzZGZlNDg5OTZhYmMwZTQ1ZjVlYzM5ZTQzMjZkMTIyZmY1OGQ2YiIsInZlcnNpb24iOjF9.fN1wSGc_tUvIgYyzKU35PuPxKyTOotKnMCW_u452LduRVyIey9KB8kf8E35vTOVvk7TCiuvRuxXDoAATFktbBQ |
|
- type: rouge |
|
value: 17.1127 |
|
name: ROUGE-L |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNWRjNTNhZjg1NDVkNTQ5MjkwZjNiNzY0Nzk5ZmM4YjhhZmZiZjQzZGY1YWM1ZGI5MGE0YjNiYzNmNWYyNWI2OSIsInZlcnNpb24iOjF9.KVGdIERnuGTOrxm71i2znI8tdRCgVz7SijP08tsE0H54eUijAYDqQccspfZTXRXeFn0lOUjSHDvHj4ODIRYvAw |
|
- type: rouge |
|
value: 23.0066 |
|
name: ROUGE-LSUM |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNGUyMzhlODY1YWI4ZDg2NzYwZDYwNmYzZTRhMTQ3NDE2MzUzZGViNzhjMTkzZDRhNTljNDEyMTY4NzAwMjE0OCIsInZlcnNpb24iOjF9.pBz5E_1ffBrv4tDCJhuYFIuBFBk0P3SKxLYoIhOVj_fW0Mj6ZKPcA9ZhdE4U-HsHEgSvFhtBw1UlsGiu145XBw |
|
- type: loss |
|
value: 2.218526601791382 |
|
name: loss |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZjYxNDk4OWU0M2Y1ZjMxNTA3NjdiNjQ5NWFjYzJiMjVhMjgzMTA3NDhlNTVjMjllZjQ0NWQ2YmYzYjdiMTQ1OCIsInZlcnNpb24iOjF9.SJdyGLltcLnB03U6QxSkZ71Im0aGK-oTbEQDMj2AnEPFThNTb0mMEMpCWpH1lLVeDAh-PE6fCmgt4yPS6n2nBg |
|
- type: gen_len |
|
value: 39.1952 |
|
name: gen_len |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTMyY2JiYWVhYTM3OWU2YjhiMDEwZjAwZDgxN2FmNjk2MzZhYmQzMWZiODg2NDY0ZmU4MjdhNjk0MTViMGY1YyIsInZlcnNpb24iOjF9.bsLAi2R8QTrCUj1VW4GQqYauY7CV3mFm2S294zHCJU2ZlAcikutcjxAcgvuSSGiAVJ02Odm5bMTuzx7SYMUSAQ |
|
- task: |
|
type: summarization |
|
name: Summarization |
|
dataset: |
|
name: billsum |
|
type: billsum |
|
config: default |
|
split: test |
|
metrics: |
|
- type: rouge |
|
value: 28.0632 |
|
name: ROUGE-1 |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiY2RiODA0ZTQxYWU0NDI5YmNjZmYzYTZmY2I5NTczYzVhZjcxOTYwMWI3ZjZiMzRlZmI5ZTA5NjVkY2E4NDFlMyIsInZlcnNpb24iOjF9.POIQUXGryoEzHmdBCeqaBh70uz33XlKVLjfhyRFwhWj7UV15SsDcuumkEk2BXkShFHDRo0CQd1AXD1fFsPCVCQ |
|
- type: rouge |
|
value: 9.8996 |
|
name: ROUGE-2 |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZDBiMDllNTZlZmJiYWI1ZTIxM2JhYmZhYTAzYTQ0NmUzNjcyZjkzMDliYTE5ZjIwY2M0YzU2ZWZlYjNhZDY2YyIsInZlcnNpb24iOjF9.EEJO-ZRVi2EiM-uKMvimaITiHh7wqzNBza6lsIvdyVhVf4UwGhsUaArHzlYR7xn53UBCtIDTucXX7NKFst_4Ag |
|
- type: rouge |
|
value: 18.25 |
|
name: ROUGE-L |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZTk4ZmJiYWIwYmY4MTBmNGVlMmE1YzA4N2VmYWU3NjRlNTU3YjI2YjBhOGIzNzcwZjczOTZmZGJiNjMyMjYzZiIsInZlcnNpb24iOjF9.Qx-ihTp0UuzhShqHQkiTijODUst1LO5Bi8KaQOCIiVhvywN-2Wt3bmeSNV_C0b5BXsSaHIxrWBTeSRaq5Zp_Bw |
|
- type: rouge |
|
value: 21.9053 |
|
name: ROUGE-LSUM |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTIzNGNkNTAyYTkzZjE5ZGZhZjZkYmU3Yjg2ZTVhYjY1NjZhODZjM2NkMWQ5NmJjN2UxNTZlMmJmNDNmOTczZSIsInZlcnNpb24iOjF9.6ZY8rK5bRfOZJkdvhpvOt_gW1xCoA4JsAi0-6No4y-lBaLGUo4LXpGaVcJrrvdN-S7e7yCxnA32jGCdYXzJJBA |
|
- type: loss |
|
value: 2.032966375350952 |
|
name: loss |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTM5MmQzMWZhOWIwNjNjNThhNGE4NzFiMzdhNmMzZWM4ZGYyNWE1NmZjMDVjNTBmMGRiNzYzMTc1ZDg2YTYxNCIsInZlcnNpb24iOjF9.Zqrbz7mmljH19mEep_mm4ev5FEsozIqG0eNkj3V5s85OgrHyuKOVkGKhRlqjcWfgkUlsxTpaemZDUVIR84XrBw |
|
- type: gen_len |
|
value: 48.5987 |
|
name: gen_len |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiODZjNGJiOGUzM2M3NDM3MDRmNmQ1ZjQ3ODUyZTQ2NjEyYmM0YWRhMmU4MDdkOTZmMGNkNTIyNDg3ZmQxMjA4MiIsInZlcnNpb24iOjF9.y91kl4W-QIy6lfQDl0s4h0aeV-v0iH7Y06AJBYRYrddUXRiDw2oSTHEdf14d3Hw-oZNPftzBHUJqAckwEpGFDw |
|
- task: |
|
type: summarization |
|
name: Summarization |
|
dataset: |
|
name: big_patent |
|
type: big_patent |
|
config: y |
|
split: test |
|
metrics: |
|
- type: rouge |
|
value: 34.7848 |
|
name: ROUGE-1 |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiN2QxOTU1YTUxYWJjOTcwOGYwZjA3MGJlYmRhYWUwMGNhYmQxYjVmMjU5M2E5OGRiY2RmMTNmNGNhZDdmNzc1OCIsInZlcnNpb24iOjF9.bp2K7V-BDMQMd3zk2RY3pILKI7LimWrD5psesXnSF20JiRA3d5-bQdOfPeZGu3ydUqbml3MTswM0lg_6EZTjAw |
|
- type: rouge |
|
value: 9.7549 |
|
name: ROUGE-2 |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOGY4OWM4MjVmMzhmNGUwYzAxODNjMjY4OTY1YjQ2MjZiYzM2NzgyNGZhMjVlNjllZmI3OTMzNTVhZDA1YzMyOSIsInZlcnNpb24iOjF9.HQ_emvr1RVEfeNfQUdfhfzk2O5BGwzpQKojvRW_w44Ixakn_VrZ4GurxYo0JTF4dDwDBDqjaFnZ4EiYcsrxODQ |
|
- type: rouge |
|
value: 22.228 |
|
name: ROUGE-L |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNWVkMzc2ODM1ZTg2YzQ4YjMzZjQwMThiODI0YzA5MzJmZjY1ZTJlOGZhOTM1OWEzOTE3M2ExYzFiMjM2NDRlMSIsInZlcnNpb24iOjF9.shmWrR-rNKAYOqEgnnlrgWXaWAWbvrKC_IyvK-fwnqoJcphB9ef2gVX758tQgfe878M1N1sE7StT8rd7FbD8Cw |
|
- type: rouge |
|
value: 28.0389 |
|
name: ROUGE-LSUM |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTJmZTczZDc4N2ZlNDk3NmY0Njc2Y2JhNGU2OWJjZGU4YWQ3Y2RjNDU1ZTEyNjFiZDQxZGNhZWFmYTAwOTBiMSIsInZlcnNpb24iOjF9.yOTMgX1vpuhlyPkfCAyNf1k5nSInny0YrbqJeC_MDZlavVIxOQT6qVcMYJpLF2AKRp6UsuFB06PANbQu4Bj6CA |
|
- type: loss |
|
value: 1.7787292003631592 |
|
name: loss |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiY2VlMGU3NDE0NmFiNTI2M2NhZmU2YzRhYjU1ZWNjYmM3YTllMTQxODJhM2JlMjk3NzVjYjQ5M2FlOTk2NjNmZCIsInZlcnNpb24iOjF9.wkkUrosSgGkei41n6CxQH_UwS6fJTMzXLV88EgnI_8Y6Qz2qa9B2cGhpFkP__snnX6u9jhWj68oAfZifqaXnCw |
|
- type: gen_len |
|
value: 71.6372 |
|
name: gen_len |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiODI0NTcwOTZmNzkwOTFhMTdmYWMxYjI2YTdmMTYwYTBlMTEyOTc3MmFkYmZkNGJmYjc4MTJlYmYwNzIxMjkzMCIsInZlcnNpb24iOjF9.EM9Vh5Mb6H3htv45ohj6FYqBUtmQi1sn0j97brEFWRYp--8N2Y781cR9ktqylEz6PgbbwpuxMYOMD5MctmGLCw |
|
- task: |
|
type: summarization |
|
name: Summarization |
|
dataset: |
|
name: launch/gov_report |
|
type: launch/gov_report |
|
config: plain_text |
|
split: validation |
|
metrics: |
|
- type: rouge |
|
value: 23.5925 |
|
name: ROUGE-1 |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNjk2NWVkM2Y5NTgxYjgyYmY2YjgzYjZiN2VhZGVkOWNiYmRiZGU0ZGYwNzlkN2E3ZDk5ZjQ3ZTQyYjU5YzczYSIsInZlcnNpb24iOjF9.ScWumfXg7-ZeJEZT1IHDVt5LWMDZEihiiCux5chXP2AeRs3pWKhI6xr_D5i3CCEDeyiMzKleCASMBe4sC9LgDQ |
|
- type: rouge |
|
value: 5.6762 |
|
name: ROUGE-2 |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMDU3MGNmMDY3YWQxNDdlMTk5MjM1NGU4M2RmNDNiYzllYmRmNTYzZGFiOGU5MjQ0YWMzYTg1OWFlNmNmMzQ5NiIsInZlcnNpb24iOjF9.9SKt_I8WGKu6bsovBR4mSTDNEaSHB1tN5RyY3JTCHYs2YQNczaKwLNPnyG2i0IbkvaPX_8EOQ7KzwQ5raUVFBg |
|
- type: rouge |
|
value: 13.8108 |
|
name: ROUGE-L |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYTBiZDVkYjI4ZDBlZGM2NDM4M2U2NzdjNzViNDkzZjE3YTBmYzdlNDNlMTZhZTUxNjA2NmJkODE2ZTk1MTAxMSIsInZlcnNpb24iOjF9.KMTkQsI9BfDfL7FZpwZ9kxTTRA8DNrUEpyBZtloQ0sNfhO0t0Ch1qhktz0HaA0uQfC0WFRfrb9Iz7uMc8XVRBg |
|
- type: rouge |
|
value: 20.2437 |
|
name: ROUGE-LSUM |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjBkZGJlYzZjMjQ1Njg4MjQ2NzJhYjY5ZGZlN2Y5Y2M4MDQ0YzQ3YzQzYmY5N2VkNjBiNTEwMDNmZWRlMTAwYyIsInZlcnNpb24iOjF9.AqYAfIMFBY7AIP1yJbjaAbJXYs5VbXxWKpsA_rdW_HWxITvjqoJDK9X3wCueXMy7dSE6L-ysC4yl99Bbc50KBA |
|
- type: loss |
|
value: 2.6377077102661133 |
|
name: loss |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNGEzMTZhODM0Nzg0ZDY3OTVkYmZmODQ1Y2YzMTY3YmJlYjk2ZGRiMWFkMDQxMTkyYTgwZWNkNmU0NzI0NjA1NCIsInZlcnNpb24iOjF9.ziVXhWBRAml5Xwa-tx9ywwtiJeIzIIclY532L0Mtft3Sc88oGPK9av6nh4kMiO5yWSHJnM3KFQWiuco7w_xNDg |
|
- type: gen_len |
|
value: 64.1807 |
|
name: gen_len |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZDNhZTRhODgwODI1YmRlODZiY2I3YjFmY2MyZGYyYTY1MzQ5OTgwZGI1NmUwNDMwMmQ0N2Y3YmZmMzcyMTc2NSIsInZlcnNpb24iOjF9.NCVj0Uaq3-diq6pnu8EC0tgwv24NwQCgmWiqpOMvJSN17B_98z_dMbLHRzY8e_tNNVFFagiCnknoE00OqUTjDg |
|
- task: |
|
type: summarization |
|
name: Summarization |
|
dataset: |
|
name: launch/gov_report |
|
type: launch/gov_report |
|
config: plain_text |
|
split: test |
|
metrics: |
|
- type: rouge |
|
value: 23.7438 |
|
name: ROUGE-1 |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzIwMzZjOGQ5N2U3MTg3NmEwYzZkNjllNDc4NzQ4NWUxN2JmYjdiOGU2MjhkZGJhODY4NDU4N2E5ODU1NTFhMiIsInZlcnNpb24iOjF9.cJoHXGYopFoFVmQXdxu3KrG_usk1ouc0PPR6FS9HrZEbi2T5LtVANntlXmlLTXSvOEaorUyg08yot_j6j1oeCw |
|
- type: rouge |
|
value: 5.501 |
|
name: ROUGE-2 |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOWQ3YmQ5ZTJkNmJhZGEyMTkzYjlkMWZmZGVhNGE5Y2IzYzA5OWM1NTY0NTU0MWUzYTIzNTQ0OGI3ZWZkNjlkMSIsInZlcnNpb24iOjF9.C_SbNoz5qIo0CtVPL_5jqFNZxgmJ1XE43TvVz2reog2jtlhekNfN0rvaHxT4TadAAzIgDZayeBMeNaASgmNCDA |
|
- type: rouge |
|
value: 13.8132 |
|
name: ROUGE-L |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZTAxODA2NmNlNjkyYTQ4YjEwOTA1ZGMyMjVlZjkzMGI3NzNiMTRkZGRmNDJjZDc2MTYxYzI3NTBlNTVjY2IxNCIsInZlcnNpb24iOjF9.UklkyvqHV3axZ_PalbPb1JZN7rgQjHjJr0ke1yDUzujrY6yBr3XpPxjFhwsEElalc1iiEgdtEZnaCbBhskdGBQ |
|
- type: rouge |
|
value: 20.4615 |
|
name: ROUGE-LSUM |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNmNhZDI2ODQ4MjBhZDNlZjJkMTQ1NmZjZTdjMDZlMjcwYjE4M2M5ZjIxYzA2M2JmYmJmZDliZTU3NzVkMjdmZiIsInZlcnNpb24iOjF9.m2aRMFUpPFvMSf3sxB7HbKIslWrggFamjiIlOAiPuH5_N8wyLJeHJJw8uvULE8R0GKGWuqXfCCv--lyhZKZkAA |
|
- type: loss |
|
value: 2.6383883953094482 |
|
name: loss |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTQzMjFiZWE1NDI1OTFlNWUxMzFiYjJhNzViNDYxMzI3OGU2ZTE1ZDJkNDA3Y2NhODA0ZWM3ZmM3ZTM1NmFlZiIsInZlcnNpb24iOjF9.twTQ94T2Nsq0__TcHLaJ_8HcqozA_FOi6pAiM_IP5qSqKlUXYV1S2-nuS1vs69QB-tSp4XIbqRqhSgKv0VoABw |
|
- type: gen_len |
|
value: 64.9085 |
|
name: gen_len |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMDk3Njc5ZWM3ZTRkMzk2YjJmMjg1YjFlNDExNTU2NTRhNzRlNjA4NGFkZDg2YmQzN2UzNThhODFmZTNlMjdkZiIsInZlcnNpb24iOjF9.2rXKy4mi2VbZRDewY2mKsVe42KuwxIWcmIzdA39RbSJ7Wg45MfRDUjZweyz7Bnlmy6eCcdv7Ya4oyUwAjNV3AQ |
|
--- |
|
|
|
# README - long-t5-tglobal-base-16384-booksum-V11-big_patent-V2 |
|
- this README was added because there wasn't one |
|
- created 2022-07-31_12-14-50 |
|
|
|
|
|
## about |
|
|
|
An experiment testing some transfer learning with [pszemraj/long-t5-tglobal-base-16384-book-summary](https://huggingface.co/pszemraj/long-t5-tglobal-base-16384-book-summary) to evaluate the ability to learn some technical documentation through the `big_patent` dataset on huggingface. |
|
|
|
This checkpoint has been trained on dataset subsection `y` of `big_patent` for approx 400 steps of functional batch size 128. |
