File size: 26,670 Bytes
923125f 683bb6c db7f095 923125f 683bb6c b6289aa 683bb6c 8ae4075 db7f095 8ae4075 db7f095 8ae4075 683bb6c 2e4030b 8ae4075 2e4030b fb68d3b 8ae4075 d9335a0 8ae4075 db7f095 8ae4075 db7f095 8ae4075 db7f095 8ae4075 db7f095 8ae4075 db7f095 8ae4075 db7f095 8ae4075 db7f095 8ae4075 db7f095 8ae4075 db7f095 8ae4075 db7f095 8ae4075 db7f095 8ae4075 db7f095 8ae4075 db7f095 b54a587 db7f095 f1bdacd db7f095 b54a587 db7f095 f1bdacd db7f095 b54a587 db7f095 f1bdacd db7f095 b54a587 db7f095 f1bdacd db7f095 b54a587 db7f095 b54a587 db7f095 b54a587 db7f095 b54a587 db7f095 b54a587 db7f095 40b097c db7f095 40b097c db7f095 40b097c db7f095 40b097c db7f095 40b097c db7f095 40b097c db7f095 40b097c db7f095 40b097c db7f095 40b097c db7f095 40b097c db7f095 40b097c db7f095 40b097c db7f095 40b097c db7f095 7054526 db7f095 7054526 db7f095 7054526 db7f095 7054526 db7f095 7054526 db7f095 7054526 db7f095 7054526 db7f095 7054526 db7f095 7054526 db7f095 7054526 db7f095 7054526 db7f095 7054526 db7f095 7054526 db7f095 683bb6c bfa004d 923125f b7c31bc 0ce67b2 bfa004d 80d13fb bfa004d 80d13fb 6a8855d b46b892 3381aec 6a8855d 3381aec 6a8855d fb142a6 3381aec 94ac8b3 3381aec af2d091 3381aec 94ac8b3 3381aec 6a8855d 3381aec 94ac8b3 3381aec 923125f 3381aec b7c31bc 3381aec 923125f 6a8855d 47f9b27 c3445a4 0ce67b2 6a8855d 47f9b27 6a8855d 923125f 6a8855d 923125f c3445a4 923125f 6a8855d 47f9b27 c3445a4 923125f 6a8855d 47f9b27 6a8855d 923125f 6a8855d 47f9b27 6a8855d 47f9b27 6a8855d 47f9b27 6a8855d 923125f 6a8855d 923125f 6a8855d 923125f 6a8855d 923125f 6a8855d bfa004d 6a8855d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 |
---
language:
- en
license:
- apache-2.0
- bsd-3-clause
tags:
- summarization
- led
- summary
- longformer
- booksum
- long-document
- long-form
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 € 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: ' the big variety of data coming from diverse sources is one of the key properties
of the big data phenomenon. It is, therefore, beneficial to understand how data
is generated in various environments and scenarios, before looking at what should
be done with this data and how to design the best possible architecture to accomplish
this The evolution of IT architectures, described in Chapter 2, means that the
data is no longer processed by a few big monolith systems, but rather by a group
of services In parallel to the processing layer, the underlying data storage has
also changed and became more distributed This, in turn, required a significant
paradigm shift as the traditional approach to transactions (ACID) could no longer
be supported. On top of this, cloud computing is becoming a major approach with
the benefits of reducing costs and providing on-demand scalability but at the
same time introducing concerns about privacy, data ownership, etc In the meantime
the Internet continues its exponential growth: Every day both structured and unstructured
data is published and available for processing: To achieve competitive advantage
companies have to relate their corporate resources to external services, e.g.
financial markets, weather forecasts, social media, etc While several of the sites
provide some sort of API to access the data in a more orderly fashion; countless
sources require advanced web mining and Natural Language Processing (NLP) processing
techniques: Advances in science push researchers to construct new instruments
for observing the universe O conducting experiments to understand even better
the laws of physics and other domains. Every year humans have at their disposal
new telescopes, space probes, particle accelerators, etc These instruments generate
huge streams of data, which need to be stored and analyzed. The constant drive
for efficiency in the industry motivates the introduction of new automation techniques
and process optimization: This could not be done without analyzing the precise
data that describe these processes. As more and more human tasks are automated,
machines provide rich data sets, which can be analyzed in real-time to drive efficiency
to new levels. Finally, it is now evident that the growth of the Internet of Things
is becoming a major source of data. More and more of the devices are equipped
with significant computational power and can generate a continuous data stream
from their sensors. In the subsequent sections of this chapter, we will look at
the domains described above to see what they generate in terms of data sets. We
will compare the volumes but will also look at what is characteristic and important
from their respective points of view. 3.1 The Internet is undoubtedly the largest
database ever created by humans. While several well described; cleaned, and structured
data sets have been made available through this medium, most of the resources
are of an ambiguous, unstructured, incomplete or even erroneous nature. Still,
several examples in the areas such as opinion mining, social media analysis, e-governance,
etc, clearly show the potential lying in these resources. Those who can successfully
mine and interpret the Internet data can gain unique insight and competitive advantage
in their business An important area of data analytics on the edge of corporate
IT and the Internet is Web Analytics.'
example_title: data science textbook
- 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: 'The majority of available text summarization datasets include short-form
source documents that lack long-range causal and temporal dependencies, and often
contain strong layout and stylistic biases. While relevant, such datasets will
offer limited challenges for future generations of text summarization systems.
We address these issues by introducing BookSum, a collection of datasets for long-form
narrative summarization. Our dataset covers source documents from the literature
domain, such as novels, plays and stories, and includes highly abstractive, human
written summaries on three levels of granularity of increasing difficulty: paragraph-,
chapter-, and book-level. The domain and structure of our dataset poses a unique
set of challenges for summarization systems, which include: processing very long
documents, non-trivial causal and temporal dependencies, and rich discourse structures.
To facilitate future work, we trained and evaluated multiple extractive and abstractive
summarization models as baselines for our dataset.'
example_title: BookSum Abstract
inference:
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
base_model: allenai/led-large-16384
model-index:
- name: pszemraj/led-large-book-summary
results:
- task:
type: summarization
name: Summarization
dataset:
name: kmfoda/booksum
type: kmfoda/booksum
config: kmfoda--booksum
split: test
metrics:
- type: rouge
value: 31.7308
name: ROUGE-1
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNjJmZjMxYTY0OGU3MzNjNmIzNmYyODNlNDg2ZGRhZDAzNTMwMDM5YWMxODc1OTc1ZWE3MzM2OTg1ODFhZDBkNCIsInZlcnNpb24iOjF9.B8BCKgySYVZW910_1zP0LfCpQYJbAe6loyWut76JlgZb2kV1_x9ybqtNESX0ka-lNqhYyXUNDpuS-7pTmsJVDg
- type: rouge
value: 5.3311
name: ROUGE-2
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYzViMmY4ODFjYTc5ODk5MmRhMDQ3ZDRiYWQwMDg0OTk3ZTA4NDAxYTNiNDgyMmI4NDA3ZDMwYWViOTBkODBjNyIsInZlcnNpb24iOjF9.MOhJLDcgvv93mVFL1igIgIiTAH3b2Xa4gmBObq7RF44Mmu8Kxtd1KP7rOlDVFOrtrsooGPGsyE1GMCQ2kqeMDg
- type: rouge
value: 16.1465
name: ROUGE-L
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzNjMzEwMTliZGE3ZmQ4M2UxMDAyMTY3YzJjZmMyMDYyN2YyNDM0N2VhNzI1MDc1YTg4MTRjMmEzNjVkNTk1NCIsInZlcnNpb24iOjF9.XLJ-DVKiYLlbw5E5rWADKbzUzf5fNHhlTCWPCC5dU4NI9Yeh76aR7TPt36ZzLDwTBknnR8KHqlaF8F8YAvBUAg
- type: rouge
value: 29.0883
name: ROUGE-LSUM
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTcwNzEwMmE5NjQxZTkzYmQyZDZmNzllYzYyNGI5OTMyNWMwNjdiM2I2YmM5YjdmY2E5OWQ3OTk3ZDA1MTc3YyIsInZlcnNpb24iOjF9.d6rFxjCB6RJNI_pn2DNNSjuZe4rdvj0RatkaTJRp5lP0F_AFfU5Zn9zRWzZJV7V-xMauIc4UhfdoLp9r_-CABA
- type: loss
value: 4.815707206726074
name: loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTMwMTgxMmJkODY3MjkzOWJhMzJhOTIxMWVkODhjZmM0MWUzMWQ1N2JkZjRhOTQxNmU1YWVjYzQ0MDNlZWI3OSIsInZlcnNpb24iOjF9.mkBQHYhYFfDV6F4klXGJ1dSsF-pbCs-6F9zcw6IYznwmXUjtk7m5J4Zt4JAju5LKz4YizvEcUCl_L0WddnfvDA
- type: gen_len
value: 154.9036
name: gen_len
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTc0ZmM1ZDM4MDE0MzY3MDM3OWJhNDkzZjJkZDdkMjU5M2JmMDJjYTIxODA1OTllNmY5ZWQzZDlmNWFiYzk4NiIsInZlcnNpb24iOjF9.VQ_O_xSTz870tnM08PJXQOwg9OsNNwI_HVX4S7AuW57_FzGGyRaWSuGE5SWzRS4Tur9YP0QxV4VV0Yoaoi3IAA
- task:
type: summarization
name: Summarization
dataset:
name: samsum
type: samsum
config: samsum
split: test
metrics:
- type: rouge
value: 33.4484
name: ROUGE-1
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTk4Yjg1YTc4YmY0MzBiZDU4ZjFhNzI4MjZkMWU1MzBlOWNlMjQ5ODMzY2YzYzRhYjJkMGUzNmI3ZjdkMzIzZSIsInZlcnNpb24iOjF9.AqS8A1OUiM0IZFBEGirv5F3Novk8lSUYSfPc3bYWLA6t-W7wgup3qA207eGbE5j9CkDWZ7QrSG1U6Z9A0sOqAA
- type: rouge
value: 10.4249
name: ROUGE-2
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiN2U4NjUyNTFmOGM5OTlhZDMyMTlmM2E4OWI2NGFiMDAyMGJjMzRjNWNlMGEyYWFmNTE5ZWMxM2I0ZGZmNWNmOCIsInZlcnNpb24iOjF9.SgJcHJ4qoRWXFvFiwv1PUutWktvsxQNynVPEv-GtBgxd6WI7o561ONyco5U-5tcyE_1SbSCJzz-L-R-q3cvoDA
- type: rouge
value: 24.5802
name: ROUGE-L
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZmQ5MDI5MzdiNGE5NDM0MmU5OThmZTBkNjkxMzg5N2IxNGVlODdhZTZhNjg3NzFjYWEyMzA3MTQxNjMyMjRkOCIsInZlcnNpb24iOjF9.Bg5dHqCcJjmxa-xGWNR5lD9g3quX7lKkH0pjiTd2xE5WiPoLLN2c0mYa2GovdW7__WnYwhhHC7es03jmvyZbCw
- type: rouge
value: 29.8226
name: ROUGE-LSUM
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNGFhOTEwNGM1MmZkNDk2ZjQ1Y2MyNjM3MGI5MGY3MWVkM2I0MjU2NWFiYmEwMjE4MTJlZWIwOGQ2MjQ3YjgzYSIsInZlcnNpb24iOjF9.W_aQKs10oXQdKEczJBGM3iiwJgb-VaXTpyA3sGof5WbhHf9vITAQA-xvynh5LgKtXQ1zjx737hnHgjEsu_Y0Cw
- type: loss
value: 4.176078796386719
name: loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiN2JhODQ5YTZkNDZkZGYyNGU2MzkxMWU5MTEwMGM2YmVjZTA5YzI5NTMxMDNhYjhlOTAxMzFiMDYwYmM0MjEzZCIsInZlcnNpb24iOjF9.OvZrPBOR5jhkoTGBgsInkH7j3_xpacXHDoT7UIXEnyXzadfBO-O-K6fjalLNZw8wSkbjHIFcL_6S_qTTxPsNAQ
- type: gen_len
value: 65.4005
name: gen_len
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiM2NhYjc3ZjQzNDEwYmMzOTM0ODkyZTJhZWNhNzZhYmEyZTYxMzA2YTYzMWFjOTA5ZjlhYWMzODg3NzY1ZTUwYSIsInZlcnNpb24iOjF9.vk9bgmtQFeRwdY3VXjtrJr_5wUCIeoAkI3kO0cHxhxmJo6RvUnyXiut72FuB-mlLZvqgiNkaZ-u_bh0Z3DjuCw
- task:
type: summarization
name: Summarization
dataset:
name: billsum
type: billsum
config: default
split: test
metrics:
- type: rouge
value: 40.5843
name: ROUGE-1
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTVjMDkyMWZjYTQ0NzgzNGUxZjNiMTg3NjU1MWJlNTQ2MWQ1NjE1MDk1OTU4ZjJiNGQ5ODg3Y2VlMWUyMzllNyIsInZlcnNpb24iOjF9.OhqBcVIuHk7fzmdrsWMvUe1bLeVMZVstZUoZpP7C1vR-3aIDl7r6eBmPrt5w-KcNq5p4teNPBsq7oKzbd5ZgDQ
- type: rouge
value: 17.3401
name: ROUGE-2
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNGQxYmQzMmE0OTcyNTM5NmMwNjIxNzYxZDcwMDFkYzJkOWY4YWY3NTdhZGRhZDdlMDAxNzcwODQ5OGM3Mzc1MCIsInZlcnNpb24iOjF9.Pksn25EEqvmx757N7Swrd4yXc_xU7-AMN9yNe8lrbBa-l1LoI_2PUASvnjML4f705cfuyMAfb0FkFp5WfER2AA
- type: rouge
value: 25.1256
name: ROUGE-L
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjhjYzI5MDBiMjk2NTY3MDNmZTdiOGYwMTRlYjIwZjAwMjdlNTAyYzdhYTJlODQ4MjYzYmQ3MjRlYTA2YzhhZSIsInZlcnNpb24iOjF9.1jPepsweS2bzIqDverQzzhmhFGch7gpoEGFGqQ8zW7K10aUKWFX8lt-uZAmTa1Z5ZhzyXGBzc3dReFPhWRRJBg
- type: rouge
value: 34.6619
name: ROUGE-LSUM
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiM2VkZDIxNWJjOTA0NzFjOTIwOTdjYjc1M2EyNDVjZjY2ZjY3MjIxNDk3YTc5YWExNzAwN2FhOTc1NjVhYjBkYiIsInZlcnNpb24iOjF9.8opqHSUckPohoSF9jfPTpXDz2AtDwvdMqOdIXx2kE1tkOcbLPbOBfcc8RhRR98y8S26yC6EYFhFnf03CV2ejAQ
- type: loss
value: 4.792657375335693
name: loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYTY5ZTRkMGU3OGVkODMzMDU5OWE1NTM5YjA4NDliZDlmNzc2NzZjNjFmNTA3M2EwY2NmN2E0MWJmZjQ5ZDliMiIsInZlcnNpb24iOjF9.KCKdk8xt2NWcMmYKV3-9eVEsFm9MqGllSMu9QCFJFIQlnyNXllHKdBLouoaGQz8IRYXvZKH8_TLDPIQx-31jAg
- type: gen_len
value: 163.9394
name: gen_len
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYzdkZDYyZGUzYmFkZmI2NjUwYmQ0MzZjMmIyZjI1YTFiMzM4OThiZjBiMzljOTVkZTgwMjA0NTE5OGM2YmFjMiIsInZlcnNpb24iOjF9.XyMZLUdkUIF32KTJMuv_bJswQCx_Tfg4Fx823cURUixSeoIKps8_a634AreZ3Z8kb7bfE_sFGh3rM9KWsMxlDw
- task:
type: summarization
name: Summarization
dataset:
name: multi_news
type: multi_news
config: default
split: test
metrics:
- type: rouge
value: 39.0834
name: ROUGE-1
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNjYzMmVlMDM4MTNkMTI4MjAyMTU2YTg1ZWQwNTI1MmJlNGUwZmE1NTRmYTljZTQwY2RlMjcxOTgyZGMyYTc0ZiIsInZlcnNpb24iOjF9.6yuSr7UmsFatwqQ-mEO4gmsEtWI05kGB5Ib2pnl05H1OiPT2uUwmqdUytUw8KTx9u1jv9q0cTF1cL-n2kPEJAA
- type: rouge
value: 11.4043
name: ROUGE-2
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMWI5N2U2ZWI1ODM2MWUwOTIzYTAzNmRhNDA2OWEzZWRjMGEzMjBmY2EwN2YyYzU1NWE0YjIyZDE3MWE0MmMxZCIsInZlcnNpb24iOjF9.wonuxbBl25TzEaHUH_E816nHJ1OSXKfkaq7eJzbLpsfeGwcDklxUSxZxRO7VBiBMaY3Qttf9ywmEIPp40HnpBA
- type: rouge
value: 19.1813
name: ROUGE-L
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZjU1NDZhN2NkMzZiZGJkODE4NDZiYjViOTZkNGMyNDlkNjBlZmFjYzU1N2IzMjFjYjY1MDU1Zjk2MzA0M2U4NyIsInZlcnNpb24iOjF9.bTCRzv3J9NiCh4aV23tAWGTvrdQCv_RS40zGwC4AJXtGS40cY7tJHYwBf9U9_rCetDBxqfjJpdaUbCAOglxLAA
- type: rouge
value: 35.1581
name: ROUGE-LSUM
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMDNhNTUyZjE4NjYxYjIzYThmMDM2YWNhM2QwYzY1ODI2ZTE3NmNjMmVhOTAzZjZlOWQwYzc1NzU2NDNjNzIxMyIsInZlcnNpb24iOjF9.cWlSbEBgrMN5D-fV_yL9geNMyMkIItcVO3wehNJPzFi3E0v1-4q8pnX-UgjLzto8X7JLi6as2V_HtZE4-C-CDw
- type: loss
value: 4.654905319213867
name: loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYTc5Nzk0ODhiNWUzNTAxNzk2YzZmMjU2NDliY2UzOTYyYTdmZGEyYjI5NDNhOTE0MGUxOTgxMGVjMmNhM2UyMSIsInZlcnNpb24iOjF9.eBBAebcl3AwkrjR6a8BvoSjDfpw8LWTRFjyIFHVzspvoOKVfnO8_NB_UeR_K127OwXyoZ70Z7X_aKJOe-2kTDA
- type: gen_len
value: 186.2494
name: gen_len
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOWI2NjVlYjgwYWJiMjcyMDUzMzEwNDNjZTMxMDM0MjAzMzk1ZmIwY2Q1ZDQ2Y2M5NDBlMDEzYzFkNWEyNzJmNiIsInZlcnNpb24iOjF9.iZ1Iy7FuWL4GH7LS5EylVj5eZRC3L2ZsbYQapAkMNzR_VXPoMGvoM69Hp-kU7gW55tmz2V4Qxhvoz9cM8fciBA
---
# led-large-book-summary
<a href="https://colab.research.google.com/gist/pszemraj/3eba944ddc9fc9a4a1bfb21e83b57620/summarization-token-batching.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
This model is a fine-tuned version of [allenai/led-large-16384](https://huggingface.co/allenai/led-large-16384) on the `BookSum` dataset (`kmfoda/booksum`). It aims to generalize well and be useful in summarizing lengthy text for both academic and everyday purposes.
- Handles up to 16,384 tokens input
- See the Colab demo linked above or try the [demo on Spaces](https://huggingface.co/spaces/pszemraj/summarize-long-text)
> **Note:** Due to inference API timeout constraints, outputs may be truncated before the fully summary is returned (try python or the demo)
---
## Basic Usage
To improve summary quality, use `encoder_no_repeat_ngram_size=3` when calling the pipeline object. This setting encourages the model to utilize new vocabulary and construct an abstractive summary.
Load the model into a pipeline object:
```python
import torch
from transformers import pipeline
hf_name = 'pszemraj/led-large-book-summary'
summarizer = pipeline(
"summarization",
hf_name,
device=0 if torch.cuda.is_available() else -1,
)
```
Feed the text into the pipeline object:
```python
wall_of_text = "your words here"
result = summarizer(
wall_of_text,
min_length=16,
max_length=256,
no_repeat_ngram_size=3,
encoder_no_repeat_ngram_size=3,
repetition_penalty=3.5,
num_beams=4,
early_stopping=True,
)
```
**Important:** For optimal summary quality, use the global attention mask when decoding, as demonstrated in [this community notebook](https://colab.research.google.com/drive/12INTTR6n64TzS4RrXZxMSXfrOd9Xzamo?usp=sharing), see the definition of `generate_answer(batch)`.
If you're facing computing constraints, consider using the base version [`pszemraj/led-base-book-summary`](https://huggingface.co/pszemraj/led-base-book-summary).
---
## Training Information
### Data
The model was fine-tuned on the [booksum](https://arxiv.org/abs/2105.08209) dataset. During training, the `chapter`was the input col, while the `summary_text` was the output.
### Procedure
Fine-tuning was run on the BookSum dataset across 13+ epochs. Notably, the final four epochs combined the training and validation sets as 'train' to enhance generalization.
### Hyperparameters
The training process involved different settings across stages:
- **Initial Three Epochs:** Low learning rate (5e-05), batch size of 1, 4 gradient accumulation steps, and a linear learning rate scheduler.
- **In-between Epochs:** Learning rate reduced to 4e-05, increased batch size to 2, 16 gradient accumulation steps, and switched to a cosine learning rate scheduler with a 0.05 warmup ratio.
- **Final Two Epochs:** Further reduced learning rate (2e-05), batch size reverted to 1, maintained gradient accumulation steps at 16, and continued with a cosine learning rate scheduler, albeit with a lower warmup ratio (0.03).
### Versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
---
## Simplified Usage with TextSum
To streamline the process of using this and other models, I've developed [a Python package utility](https://github.com/pszemraj/textsum) named `textsum`. This package offers simple interfaces for applying summarization models to text documents of arbitrary length.
Install TextSum:
```bash
pip install textsum
```
Then use it in Python with this model:
```python
from textsum.summarize import Summarizer
model_name = "pszemraj/led-large-book-summary"
summarizer = Summarizer(
model_name_or_path=model_name, # you can use any Seq2Seq model on the Hub
token_batch_length=4096, # tokens to batch summarize at a time, up to 16384
)
long_string = "This is a long string of text that will be summarized."
out_str = summarizer.summarize_string(long_string)
print(f"summary: {out_str}")
```
Currently implemented interfaces include a Python API, a Command-Line Interface (CLI), and a demo/web UI.
For detailed explanations and documentation, check the [README](https://github.com/pszemraj/textsum) or the [wiki](https://github.com/pszemraj/textsum/wiki)
---
## Related Models
Check out these other related models, also trained on the BookSum dataset:
- [LED-large continued](https://huggingface.co/pszemraj/led-large-book-summary-continued) - experiment with further fine-tuning
- [Long-T5-tglobal-base](https://huggingface.co/pszemraj/long-t5-tglobal-base-16384-book-summary)
- [BigBird-Pegasus-Large-K](https://huggingface.co/pszemraj/bigbird-pegasus-large-K-booksum)
- [Pegasus-X-Large](https://huggingface.co/pszemraj/pegasus-x-large-book-summary)
- [Long-T5-tglobal-XL](https://huggingface.co/pszemraj/long-t5-tglobal-xl-16384-book-summary)
There are also other variants on other datasets etc on my hf profile, feel free to try them out :)
---
|