autoevaluator
HF staff
Add evaluation results on the kmfoda--booksum config and test split of kmfoda/booksum
4675302
language: | |
- en | |
license: | |
- bsd-3-clause | |
- apache-2.0 | |
library_name: transformers | |
tags: | |
- long document summary | |
- book summary | |
- booksum | |
datasets: | |
- kmfoda/booksum | |
metrics: | |
- rouge | |
pipeline_tag: summarization | |
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 | |
- text: The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey | |
building, and the tallest structure in Paris. Its base is square, measuring 125 | |
metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed | |
the Washington Monument to become the tallest man-made structure in the world, | |
a title it held for 41 years until the Chrysler Building in New York City was | |
finished in 1930. It was the first structure to reach a height of 300 metres. | |
Due to the addition of a broadcasting aerial at the top of the tower in 1957, | |
it is now taller than the Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, | |
the Eiffel Tower is the second tallest free-standing structure in France after | |
the Millau Viaduct. | |
example_title: eiffel | |
parameters: | |
max_length: 64 | |
min_length: 8 | |
no_repeat_ngram_size: 3 | |
early_stopping: true | |
repetition_penalty: 3.5 | |
encoder_no_repeat_ngram_size: 4 | |
num_beams: 2 | |
model-index: | |
- name: pszemraj/led-large-book-summary-continued | |
results: | |
- task: | |
type: summarization | |
name: Summarization | |
dataset: | |
name: kmfoda/booksum | |
type: kmfoda/booksum | |
config: kmfoda--booksum | |
split: test | |
metrics: | |
- type: rouge | |
value: 31.2367 | |
name: ROUGE-1 | |
verified: true | |
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOWI3NzQwMTUxOWRkOGVmZGYwZTkyODIxZmRhM2Y5N2FjYmM2MWEyMDNiN2JmODc3ODExNTAwZjhhZDJkNzNiYyIsInZlcnNpb24iOjF9.EYEvooI7WG94OinI4p5sNiuM1MAFVSYeb2ehv2lGe-B-qR1yvPVBBr7J3iI5UFegZsYciCLA6VRFUe8eQ8KNAg | |
- type: rouge | |
value: 5.0148 | |
name: ROUGE-2 | |
verified: true | |
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYzMxYjIzMWY2MTNkODczZWEzOGEzNjYxNzZjMTc0N2U3NmFhMWM5NWFiMzBjZDEwNTFkYjhhMGMwMjliY2JjOSIsInZlcnNpb24iOjF9.DmIc7iNjo5nm_T-uWehMCbcWjgY_WNGdRkiUXdzv96uFIRiVIoW03UspkGfzvjEiKRoa7OM403XZxNXuCjVJCQ | |
- type: rouge | |
value: 15.7724 | |
name: ROUGE-L | |
verified: true | |
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDUzNzNkYjUxMjE1MzZjMDhkNWE2MmZlMTg0OGM1NDc2M2JlZDJmNDI3M2YyZGM2NmY1ZDZlOWYxMzcyYmExZCIsInZlcnNpb24iOjF9.CVjivCusq1J_tiktqQ-pnsH6iOWdYrf5rwt9wlGoCgw4boXzDVivtHpe0MWlJ5L-XFY75SnrMXeunCBGOwONBQ | |
- type: rouge | |
value: 28.494 | |
name: ROUGE-LSUM | |
verified: true | |
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYTY0MjI3NDNkYzI5ZjA1Nzg5MmE0MzY3OTZkM2U2ZWZkMDBjZjQzMjdjN2Q3Y2NiZjIwNzI1OWJhMzhjYzg4NiIsInZlcnNpb24iOjF9.A0iwWEti-OPFbi9TEpnEpC0rPCLP3Gw3Ns23Lz8e_zi4B_vlGrVW7weofzO8cuGVoC9kS-aJk2a5VGdXYh5KBw | |
- type: loss | |
value: 4.777158260345459 | |
name: loss | |
verified: true | |
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZWZkNjdhNGNkNDUyYWNlNDgyNzkxNDdkNTZlOGQ0MmQ3ZGVjYjgwZTk2M2E4NjAwNWZkNGEzMTU2ZWFjMmFmMCIsInZlcnNpb24iOjF9.TTEWfYmpM4VPKn1Jukkwadj6C3HASvzTMJeTLHCHqd5Vr7s0X0PcIKvnyEVycwywFanfrgIg4Pyn0G_IVeYcBg | |
- type: gen_len | |
value: 154.1908 | |
name: gen_len | |
verified: true | |
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMmI3YjZkNTZmMzNjMzMzODlhODFmNWFlNjNmODI0ZjE2ZWNjMzcxMWUyMGMzNzY2MDIzZWIwYTMxODk3M2Q3YiIsInZlcnNpb24iOjF9.nyUANcwiu-sb3vXMFIdzvdDPTBBhJOEQmdu25XSXRgwNSfugKDydAoHy2tdo9ZE8r32xxYDPoutER22APV4PCA | |
# led-large-book-summary: continued | |
Fine-tuned further to explore if any improvements vs. the default. | |
## Details | |
This model is a version of [pszemraj/led-large-book-summary](https://huggingface.co/pszemraj/led-large-book-summary) further fine-tuned for two epochs. | |
## Usage | |
It's recommended to use this model with [beam search decoding](https://huggingface.co/docs/transformers/generation_strategies#beamsearch-decoding). If interested, you can also use the `textsum` util repo to have most of this abstracted out for you: | |
```bash | |
pip install -U textsum | |
``` | |
```python | |
from textsum.summarize import Summarizer | |
model_name = "pszemraj/led-large-book-summary-continued" | |
summarizer = Summarizer(model_name) # GPU auto-detected | |
text = "put the text you don't want to read here" | |
summary = summarizer.summarize_string(text) | |
print(summary) | |
``` | |
## Training procedure | |
### Training hyperparameters | |
The following hyperparameters were used during training: | |
- learning_rate: 3e-05 | |
- train_batch_size: 4 | |
- eval_batch_size: 2 | |
- seed: 8191 | |
- gradient_accumulation_steps: 16 | |
- total_train_batch_size: 64 | |
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
- lr_scheduler_type: cosine | |
- lr_scheduler_warmup_ratio: 0.01 | |
- num_epochs: 2.0 | |
- mixed_precision_training: Native AMP | |