pszemraj's picture
Add evaluation results on the 3.0.0 config and test split of cnn_dailymail (#17)
- en
- apache-2.0
- bsd-3-clause
- summarization
- led
- summary
- longformer
- booksum
- long-document
- long-form
- kmfoda/booksum
- rouge
- 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
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
- name: pszemraj/led-large-book-summary
- task:
type: summarization
name: Summarization
name: kmfoda/booksum
type: kmfoda/booksum
config: kmfoda--booksum
split: test
- type: rouge
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- task:
type: summarization
name: Summarization
name: samsum
type: samsum
config: samsum
split: test
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name: Summarization
name: billsum
type: billsum
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split: test
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name: Summarization
name: multi_news
type: multi_news
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name: Summarization
name: cnn_dailymail
type: cnn_dailymail
config: 3.0.0
split: test
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# led-large-book-summary
<a href="">
<img src="" alt="Open In Colab"/>
This model is a fine-tuned version of [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](
> **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:
import torch
from transformers import pipeline
hf_name = 'pszemraj/led-large-book-summary'
summarizer = pipeline(
device=0 if torch.cuda.is_available() else -1,
Feed the text into the pipeline object:
wall_of_text = "your words here"
result = summarizer(
**Important:** For optimal summary quality, use the global attention mask when decoding, as demonstrated in [this community notebook](, see the definition of `generate_answer(batch)`.
If you're facing computing constraints, consider using the base version [`pszemraj/led-base-book-summary`](
## Training Information
### Data
The model was fine-tuned on the [booksum]( 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]( named `textsum`. This package offers simple interfaces for applying summarization models to text documents of arbitrary length.
Install TextSum:
pip install textsum
Then use it in Python with this model:
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]( or the [wiki](
## Related Models
Check out these other related models, also trained on the BookSum dataset:
- [LED-large continued]( - experiment with further fine-tuning
- [Long-T5-tglobal-base](
- [BigBird-Pegasus-Large-K](
- [Pegasus-X-Large](
- [Long-T5-tglobal-XL](
There are also other variants on other datasets etc on my hf profile, feel free to try them out :)