metadata
language:
- en
tags:
- summarization
- led
- summary
- longformer
- booksum
- long-document
- long-form
license: apache-2.0
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
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:
- name: ROUGE-1
type: rouge
value: 31.7308
verified: true
- name: ROUGE-2
type: rouge
value: 5.3311
verified: true
- name: ROUGE-L
type: rouge
value: 16.1465
verified: true
- name: ROUGE-LSUM
type: rouge
value: 29.0883
verified: true
- name: loss
type: loss
value: 4.815707206726074
verified: true
- name: gen_len
type: gen_len
value: 154.9036
verified: true
- task:
type: summarization
name: Summarization
dataset:
name: samsum
type: samsum
config: samsum
split: test
metrics:
- name: ROUGE-1
type: rouge
value: 33.4484
verified: true
- name: ROUGE-2
type: rouge
value: 10.4249
verified: true
- name: ROUGE-L
type: rouge
value: 24.5802
verified: true
- name: ROUGE-LSUM
type: rouge
value: 29.8226
verified: true
- name: loss
type: loss
value: 4.176078796386719
verified: true
- name: gen_len
type: gen_len
value: 65.4005
verified: true
- task:
type: summarization
name: Summarization
dataset:
name: billsum
type: billsum
config: default
split: test
metrics:
- name: ROUGE-1
type: rouge
value: 40.5843
verified: true
- name: ROUGE-2
type: rouge
value: 17.3401
verified: true
- name: ROUGE-L
type: rouge
value: 25.1256
verified: true
- name: ROUGE-LSUM
type: rouge
value: 34.6619
verified: true
- name: loss
type: loss
value: 4.792657375335693
verified: true
- name: gen_len
type: gen_len
value: 163.9394
verified: true
Longformer Encoder-Decoder (LED) fine-tuned on Booksum
demo:
- A fine-tuned version of allenai/led-large-16384 on the BookSum dataset.
- Goal: a model that can generalize well and is useful in summarizing long text in academic and daily usage.
- works well on lots of text and can handle 16384 tokens/batch (if you have the GPU memory to handle that)
Note: the API is set to generate a max of 64 tokens for runtime reasons, so the summaries may be truncated (depending on length of input text). For best results use python as below.
Usage - Basic
- use
encoder_no_repeat_ngram_size=3when calling the pipeline object to improve summary quality.- this forces the model to use new vocabulary and create an abstractive summary, otherwise it may compile the best extractive summary from the input provided.
Load the model into a pipeline object:
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,
)
- put words into the pipeline object:
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: To generate the best quality summaries, you should use the global attention mask when decoding, as demonstrated in this community notebook here, see the definition of generate_answer(batch).
If having computing constraints, try the base version pszemraj/led-base-book-summary
- all the parameters for generation on the API here are the same as the base model for easy comparison between versions.
Training and evaluation data
- the booksum dataset
- During training, the input text was the text of the
chapter, and the output wassummary_text - Eval results can be found here with metrics on the sidebar.
Training procedure
- Training completed on the BookSum dataset for 13 total epochs
- The final four epochs combined the training and validation sets as 'train' in an effort to increase generalization.
Training hyperparameters
Initial Three Epochs
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
In-between Epochs
Unfortunately, don't have all records on-hand for middle epochs, the following should be representative:
- learning_rate: 4e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 6 (in addition to prior model)
Final Two Epochs
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 2 (in addition to prior model)
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1