remove inference

README.md

@@ -10,165 +10,7 @@ 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
-- 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
-parameters:
-  max_length: 64
-  min_length: 4
-  no_repeat_ngram_size: 3
-  early_stopping: true
-  repetition_penalty: 1.5
-  length_penalty: 0.8
-  encoder_no_repeat_ngram_size: 4
-  num_beams: 1
+inference: False
 model-index:
 - name: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP
   results:
@@ -260,5 +102,5 @@ _As I make updates to this WIP checkpoint I will post a note here._
  ## Comparisons
  
  - compare to [pszemraj/led-large-book-summary](https://huggingface.co/pszemraj/led-large-book-summary). 
-   - **the inference parameters for LongT5 are "worse" (i.e. lower number of beams) for the Large model to enable inference via the API**
+   - **inference API has been disabled because it's too compute-intensive :/**