Add evaluation results on the kmfoda--booksum config of kmfoda/booksum (#2)

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	---
	language:
	- en
	tags:
	- summarization
	- summarisation
	- summary
	- notes
	- bigbird_pegasus_
	- pegasus
	- bigbird
	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 \u20AC 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 \u20AC 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 \U0001F917's recent blog post in case you are unfamiliar with these\
	\ models.\nBigBird (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.\nBigBird RoBERTa-like model is now available in \U0001F917\
	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 \U0001F917\
	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 \u221E compute & \u221E time, BERT's attention\
	\ would be preferred over block sparse attention (which we are going to discuss\
	\ in this post).\nIf you wonder why we need more compute when working with longer\
	\ sequences, this blog post is just right for you!\nSome of the main questions\
	\ one might have when working with standard BERT-like attention include:\nDo 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.\nWhat 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.\nLet'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.\n>>> # let's\
	\ consider following sentence as an example >>> example = ['BigBird', 'is', 'now',\
	\ 'available', 'in', 'HuggingFace', 'for', 'extractive', 'question', 'answering']\n\
	>>> # 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
	inference:
	parameters:
	max_length: 64
	no_repeat_ngram_size: 2
	encoder_no_repeat_ngram_size: 3
	repetition_penalty: 2.4
	length_penalty: 0.5
	num_beams: 4
	early_stopping: true
	model-index:
	- name: pszemraj/bigbird-pegasus-large-K-booksum
	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: 34.0757
	verified: true
	- name: ROUGE-2
	type: rouge
	value: 5.9177
	verified: true
	- name: ROUGE-L
	type: rouge
	value: 16.3874
	verified: true
	- name: ROUGE-LSUM
	type: rouge
	value: 31.6118
	verified: true
	- name: loss
	type: loss
	value: 3.522040605545044
	verified: true
	- name: gen_len
	type: gen_len
	value: 254.3676
	verified: true
	- task:
	type: summarization
	name: Summarization
	dataset:
	name: launch/gov_report
	type: launch/gov_report
	config: plain_text
	split: test
	metrics:
	- name: ROUGE-1
	type: rouge
	value: 40.015
	verified: true
	- name: ROUGE-2
	type: rouge
	value: 10.7406
	verified: true
	- name: ROUGE-L
	type: rouge
	value: 20.1344
	verified: true
	- name: ROUGE-LSUM
	type: rouge
	value: 36.7743
	verified: true
	- name: loss
	type: loss
	value: 3.8273396492004395
	verified: true
	- name: gen_len
	type: gen_len
	value: 228.1285
	verified: true
	---


	# bigbird pegasus on the booksum dataset

	>_this is the "latest" version of the model that has been trained the longest, currently at 70k steps_

	- GOAL: A summarization model that 1) summarizes the source content accurately 2) _more important IMO_ produces summaries that are easy to read and understand (* cough * unlike arXiv * cough *)
	- This model attempts to help with that by using the [booksum](https://arxiv.org/abs/2105.08209) dataset to provide explanatory summarization
	- Explanatory Summary - A summary that both consolidates information and also explains why said consolidated information is important.
	- This model was trained for seven epochs total (approx 70,000 steps) and is closer to finished.
	- Will continue to improve (slowly, now that it has been trained for a long time) based on any result findings/feedback.
	- starting checkpoint was `google/bigbird-pegasus-large-bigpatent`

	---

	# example usage

	> An extended example, including a demo of batch summarization, is [here](https://colab.research.google.com/gist/pszemraj/2c8c0aecbcd4af6e9cbb51e195be10e2/bigbird-pegasus-large-booksum-20k-example.ipynb).


	- create the summarizer object:

	```
	from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
	from transformers import pipeline

	_model = AutoModelForSeq2SeqLM.from_pretrained(
	"pszemraj/bigbird-pegasus-large-K-booksum",
	low_cpu_mem_usage=True,
	)

	_tokenizer = AutoTokenizer.from_pretrained(
	"pszemraj/bigbird-pegasus-large-K-booksum",
	)


	summarizer = pipeline(
	"summarization",
	model=_model,
	tokenizer=_tokenizer
	)

	```

	- define text to be summarized, and pass it through the pipeline. Boom done.

	```
	wall_of_text = "your text to be summarized goes here."

	result = summarizer(
	wall_of_text,
	min_length=16,
	max_length=256,
	no_repeat_ngram_size=3,
	clean_up_tokenization_spaces=True,
	)

	print(result[0]['summary_text'])
	```

	## Alternate Checkpoint

	- if experiencing runtime/memory issues, try [this earlier checkpoint](https://huggingface.co/pszemraj/bigbird-pegasus-large-booksum-40k-K) at 40,000 steps which is almost as good at the explanatory summarization task but runs faster.

	---

	# Results

	- note that while the dataset has three subsets (chapter, book, paragraph) - see the [paper](https://arxiv.org/abs/2105.08209). the scores below are run in aggregate. The paper has some benchmark scores listed, which this model competes with.
	- note that eval generations are run & computed at a length of 128 tokens.


	```
	'eval_gen_len': 126.9791,
	'eval_loss': 4.00944709777832,
	'eval_rouge1': 27.6028,
	'eval_rouge2': 4.6556,
	'eval_rougeL': 14.5259,
	'eval_rougeLsum': 25.6632,
	'eval_runtime': 29847.4812,
	'eval_samples_per_second': 0.05,
	'eval_steps_per_second': 0.05}

	```