add one more example and clarify readme

README.md

@@ -4,6 +4,9 @@ language:
 - en
 tags:
 - summarization
+- summarisation
+- summary
+- notes
 - bigbird_pegasus_
 - pegasus
 - bigbird
@@ -19,7 +22,38 @@ widget:
   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"
+  
 inference:
   parameters:
     max_length: 64
@@ -38,12 +72,12 @@ inference:
 
 >_this is the "latest" version of the model that has been trained the longest, currently at 70k steps_
 
-- motivation: typical datasets for summarization models are in the vein of PubMed / arXiv; for my use cases, I have found summaries created by models pretrained on these to be mostly useless.
-  - summarizing text via arXiv models will typically make the summary sound so needlessly complicated that you might as well have read the original text in that time.
-  - this model is one attempt to help with that by using the [booksum](https://arxiv.org/abs/2105.08209) dataset to provide **explanatory summarization**
+- 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 has been 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.
-- the starting checkpoint was `google/bigbird-pegasus-large-bigpatent`
+- starting checkpoint was `google/bigbird-pegasus-large-bigpatent`
 
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