README.md

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: 33.4514
            verified: true
          - name: ROUGE-2
            type: rouge
            value: 10.3718
            verified: true
          - name: ROUGE-L
            type: rouge
            value: 24.5432
            verified: true
          - name: ROUGE-LSUM
            type: rouge
            value: 29.7913
            verified: true
          - name: loss
            type: loss
            value: 4.176078796386719
            verified: true
          - name: gen_len
            type: gen_len
            value: 65.4005
            verified: true

Longformer Encoder-Decoder (LED) fine-tuned on Booksum

• This model is a fine-tuned version of allenai/led-large-16384 on the booksum dataset.
• the goal was to create a model that can generalize well and is useful in summarizing lots of text in academic and daily usage.
• I don't want to read anymore give me a notebook implementation
• all the parameters for generation on the API are the same as the base model for easy comparison between versions.
• works well on lots of text, can hand 16384 tokens/batch.

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 - Basics

• it is recommended to use encoder_no_repeat_ngram_size=3 when calling the pipeline object to improve summary quality.
  • this param forces the model to use new vocabulary and create an abstractive summary, otherwise it may compile the best extractive summary from the input provided.
• create the pipeline object:

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from transformers import pipeline

hf_name = 'pszemraj/led-large-book-summary'

_model = AutoModelForSeq2SeqLM.from_pretrained(
                hf_name,
                low_cpu_mem_usage=True,
            )

_tokenizer = AutoTokenizer.from_pretrained(
                hf_name
            )
                                           

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

• 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,
           clean_up_tokenization_spaces=True,
           repetition_penalty=3.7,
           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).

Training and evaluation data

• the booksum dataset
• During training, the input text was the text of the chapter, and the output was summary_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