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--- |
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license: |
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- apache-2.0 |
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- bsd-3-clause |
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tags: |
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- summarization |
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- led |
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- summary |
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- longformer |
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- booksum |
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- long-document |
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- long-form |
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datasets: |
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- kmfoda/booksum |
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metrics: |
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- rouge |
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widget: |
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- text: large earthquakes along a given fault segment do not occur at random intervals |
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because it takes time to accumulate the strain energy for the rupture. The rates |
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at which tectonic plates move and accumulate strain at their boundaries are approximately |
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uniform. Therefore, in first approximation, one may expect that large ruptures |
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of the same fault segment will occur at approximately constant time intervals. |
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If subsequent main shocks have different amounts of slip across the fault, then |
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the recurrence time may vary, and the basic idea of periodic mainshocks must be |
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modified. For great plate boundary ruptures the length and slip often vary by |
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a factor of 2. Along the southern segment of the San Andreas fault the recurrence |
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interval is 145 years with variations of several decades. The smaller the standard |
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deviation of the average recurrence interval, the more specific could be the long |
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term prediction of a future mainshock. |
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example_title: earthquakes |
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- text: ' A typical feed-forward neural field algorithm. Spatiotemporal coordinates |
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are fed into a neural network that predicts values in the reconstructed domain. |
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Then, this domain is mapped to the sensor domain where sensor measurements are |
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available as supervision. Class and Section Problems Addressed Generalization |
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(Section 2) Inverse problems, ill-posed problems, editability; symmetries. Hybrid |
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Representations (Section 3) Computation & memory efficiency, representation capacity, |
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editability: Forward Maps (Section 4) Inverse problems Network Architecture (Section |
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5) Spectral bias, integration & derivatives. Manipulating Neural Fields (Section |
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6) Edit ability, constraints, regularization. Table 2: The five classes of techniques |
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in the neural field toolbox each addresses problems that arise in learning, inference, |
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and control. (Section 3). We can supervise reconstruction via differentiable forward |
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maps that transform Or project our domain (e.g, 3D reconstruction via 2D images; |
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Section 4) With appropriate network architecture choices, we can overcome neural |
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network spectral biases (blurriness) and efficiently compute derivatives and integrals |
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(Section 5). Finally, we can manipulate neural fields to add constraints and regularizations, |
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and to achieve editable representations (Section 6). Collectively, these classes |
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constitute a ''toolbox'' of techniques to help solve problems with neural fields |
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There are three components in a conditional neural field: (1) An encoder or inference |
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function € that outputs the conditioning latent variable 2 given an observation |
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0 E(0) =2. 2 is typically a low-dimensional vector, and is often referred to aS |
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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 |
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most probable z given the observations O: argmaxz P(2/0). The decoder maximizes |
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the inverse conditional probability to find the most probable 0 given Z: arg- |
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max P(Olz). We discuss different encoding schemes with different optimality guarantees |
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(Section 2.1.1), both global and local conditioning (Section 2.1.2), and different |
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mapping functions Y (Section 2.1.3) 2. Generalization Suppose we wish to estimate |
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a plausible 3D surface shape given a partial or noisy point cloud. We need a suitable |
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prior over the sur- face in its reconstruction domain to generalize to the partial |
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observations. A neural network expresses a prior via the function space of its |
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architecture and parameters 0, and generalization is influenced by the inductive |
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bias of this function space (Section 5).' |
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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 |
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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 |
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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 |
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same time introducing concerns about privacy, data ownership, etc In the meantime |
|
the Internet continues its exponential growth: Every day both structured and unstructured |
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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 |
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with significant computational power and can generate a continuous data stream |
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from their sensors. In the subsequent sections of this chapter, we will look at |
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the domains described above to see what they generate in terms of data sets. We |
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will compare the volumes but will also look at what is characteristic and important |
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from their respective points of view. 3.1 The Internet is undoubtedly the largest |
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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, |
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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.' |
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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 |
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& 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 |
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checkout 🤗''s recent blog post in case you are unfamiliar with these models. |
|
|
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BigBird (introduced in paper) is one of such recent models to address this issue. |
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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 |
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cost compared to BERT. It has achieved SOTA on various tasks involving very long |
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sequences such as long documents summarization, question-answering with long contexts. |
|
|
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BigBird RoBERTa-like model is now available in 🤗Transformers. The goal of this |
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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 |
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becomes unbearable. Simply put, if we would have ∞ compute & ∞ time, BERT''s attention |
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would be preferred over block sparse attention (which we are going to discuss |
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in this post). |
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|
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If you wonder why we need more compute when working with longer sequences, this |
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blog post is just right for you! |
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|
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Some of the main questions one might have when working with standard BERT-like |
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attention include: |
|
|
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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 |
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to just a few tokens in a very efficient way? In this blog post, we will try to |
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answer those questions. |
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|
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What tokens should be attended to? We will give a practical example of how attention |
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works by considering the sentence ''BigBird is now available in HuggingFace for |
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extractive question answering''. In BERT-like attention, every word would simply |
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attend to all other tokens. |
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|
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Let''s think about a sensible choice of key tokens that a queried token actually |
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only should attend to by writing some pseudo-code. Will will assume that the token |
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available is queried and build a sensible list of key tokens to attend to. |
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|
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>>> # let''s consider following sentence as an example >>> example = [''BigBird'', |
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''is'', ''now'', ''available'', ''in'', ''HuggingFace'', ''for'', ''extractive'', |
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''question'', ''answering''] |
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|
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>>> # further let''s assume, we''re trying to understand the representation of |
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''available'' i.e. >>> query_token = ''available'' >>> # We will initialize an |
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empty `set` and fill up the tokens of our interest as we proceed in this section. |
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>>> key_tokens = [] # => currently ''available'' token doesn''t have anything |
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to attend Nearby tokens should be important because, in a sentence (sequence of |
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words), the current word is highly dependent on neighboring past & future tokens. |
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This intuition is the idea behind the concept of sliding attention.' |
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example_title: bigbird blog intro |
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- text: 'The majority of available text summarization datasets include short-form |
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source documents that lack long-range causal and temporal dependencies, and often |
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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 |
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narrative summarization. Our dataset covers source documents from the literature |
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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. |
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To facilitate future work, we trained and evaluated multiple extractive and abstractive |
|
summarization models as baselines for our dataset.' |
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example_title: BookSum Abstract |
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inference: |
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parameters: |
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max_length: 64 |
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min_length: 8 |
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no_repeat_ngram_size: 3 |
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early_stopping: true |
|
repetition_penalty: 3.5 |
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length_penalty: 0.3 |
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encoder_no_repeat_ngram_size: 3 |
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num_beams: 4 |
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model-index: |
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- name: pszemraj/led-base-book-summary |
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results: |
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- task: |
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type: summarization |
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name: Summarization |
|
dataset: |
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name: kmfoda/booksum |
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type: kmfoda/booksum |
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config: kmfoda--booksum |
|
split: test |
|
metrics: |
|
- type: rouge |
|
value: 33.4536 |
|
name: ROUGE-1 |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYmEzYjNkZTUxZjA0YTdmNTJkMjVkMTg2NDRjNTkzN2ZlNDlhNTBhMWQ5MTNiYWE4Mzg5YTMyMTM5YmZjNDI3OSIsInZlcnNpb24iOjF9.OWjM_HCQLQHK4AV4em70QGT3lrVk25WyZdcXA8ywest_XSx9KehJbsIMDKtXxOOMwxvkogKnScy4tbskYMQqDg |
|
- type: rouge |
|
value: 5.2232 |
|
name: ROUGE-2 |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTVhOTdjZjc5YTdhMmVjZGE1NTA5MmJkYmM3Y2U3OGVlMjZmOGVlMTUzYTdiZGRhM2NmZjAzMjFkZjlkMzJmOCIsInZlcnNpb24iOjF9.qOlwWEe8dfBunmwImhbkcxzUW3ml-ESsuxjWN1fjn_o36zaUlDqlrXovMcL9GX9mVdvZDhx9W82rAR8h6410AQ |
|
- type: rouge |
|
value: 16.2044 |
|
name: ROUGE-L |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzkwOTEwYjkxYzlhMWE4ZjhlZDVjZWEwMWY2YzgwY2Q2YzJkYWFhMTQ4ODFlZmVkY2I1OWVhMTFmZThlOGY4NCIsInZlcnNpb24iOjF9.fJSr9wRQ07YIPMpb2_xv14EkHRz3gsPdZH-4LzpdviLOjVhlK1Y4gSZjp3PTEbu4Hua0umvNTMrhii8hp3DFBA |
|
- type: rouge |
|
value: 29.9765 |
|
name: ROUGE-LSUM |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYWRkYjcwMTYwODRjN2E4MDliZWQyNjczNDU1NGZkMDRkNDlhNDA1YzZiOTk1MWJjZDkyMDg3MGMxYmVhOTA5MyIsInZlcnNpb24iOjF9.tUkVmhT0bl9eY_BzAzdzEI1lo3Iyfv6HBrrsVsRHqPFh4C0Q9Zk3IXbR-F_gMDx9vDiZIkpfG7SfsIZXwhDkBw |
|
- type: loss |
|
value: 3.1985862255096436 |
|
name: loss |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiM2RmYzQ1NTFiYjk3YTZjMTI3NDJlMDY0MTgyZDZlZDRmZDcwOWE1YjU0OGYyZTJlY2RkZTEzZDFlNDk2ZjgyNSIsInZlcnNpb24iOjF9.Pc5Tfu8IXYeB5ETK2JMIL4gpRIvvYXVS6w1AZdfq9dD1dm9Te2xaNhzGBHviqgEfFI9APNSJB28wna1OpYP0Dg |
|
- type: gen_len |
|
value: 191.9783 |
|
name: gen_len |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNmMyMDI5MzFlNzNjODNmOWQ0ZTM3MzVkNTNkYzIxNTIwZDQzMTU2MTM0YjYzNjJiMGRhOTQ0OWFhN2U4N2NjYyIsInZlcnNpb24iOjF9.AfsX-O1YwfbPxUwAD7rd1Ub7SXth7FFpTo2iNSOUWFhYmDUECkf6qtJ5pVHXXZwnpidAlfPTPg-5y3dx_BBGCA |
|
- task: |
|
type: summarization |
|
name: Summarization |
|
dataset: |
|
name: samsum |
|
type: samsum |
|
config: samsum |
|
split: test |
|
metrics: |
|
- type: rouge |
|
value: 32 |
|
name: ROUGE-1 |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYmNhZjk3NjFlZDBhZjU2YzgzOTdhZTNkZjBkYjNjZDk2YjE2NDBmMDhiY2Y5M2EwNGI5Njk1NWU3ZDYyMzk2ZSIsInZlcnNpb24iOjF9.htkMQQLjIeFFjnpAJOwwxAdgzGZX10Und6RONubeeydXqQqb562EHqAw0K1ZlqltC4GBGKK3xslGOWXQ5AV6CA |
|
- type: rouge |
|
value: 10.0781 |
|
name: ROUGE-2 |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMWYzZDA1YmU5YTkzMjEwN2IzMTNhZmZmOTU2ZGUyNzdlNWQ0OGQ1Y2UxOGQ0NWUyOWVmZmZkYzFkODE3OTliNiIsInZlcnNpb24iOjF9.WVE3fmYLkOW32_neYYj4TNJ5lhrG-27DnoJd4YDUzpHYvGWGoFU9CUuIFraQFnojRr02f3KqVY7T33DG5mpzBg |
|
- type: rouge |
|
value: 23.6331 |
|
name: ROUGE-L |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYTYyOTE0ODY2Mjk0YTk5ZTY5NTZkM2JkOGZhNjQ3NjNiMjVhNTc4ZmMwYzg1ZGIxOTA2MDQxNmU3Yjc5YWY0MSIsInZlcnNpb24iOjF9.yQ8WpdsyGKSuTG8MxHXqujEAYOIrt_hoUbuHc8HnS-GjS9xJ-rKO6pP6HYbi0LC9Xqh2_QPveCpNqr9ZQMGRCg |
|
- type: rouge |
|
value: 28.7831 |
|
name: ROUGE-LSUM |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzVkMDNlODA4NWI3OGI1OGFlNjFlNWE4YzY5ZDE1NDdhMjIwYjlkNDIxNDZjOGRiNTI1MGJkMmE0YWZiMDNhMiIsInZlcnNpb24iOjF9.qoxn2g70rbbX6sVCvm_cXzvYZf1UdTDU44vvEVdZL-4h36cJRCOx5--O1tZEVdyvlMVi-tYz1RSxLRwQd72FAw |
|
- type: loss |
|
value: 2.903024673461914 |
|
name: loss |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZGM2M2NlY2Q3NjYxY2EyM2FkYmM5OGVhYzcyNjA3ZTFlYzc3M2M2ODNmNWVjNjZmMGNiODc4MWY5NWE2ZDMyNyIsInZlcnNpb24iOjF9.pC4UK75LbyVFFm0-fcStMtdQhbuHE37wkZHoVbSQOYSyxjI8yA46bQkPmgg5znby9FK_wIgGxC_4KOdEeN4jBw |
|
- type: gen_len |
|
value: 60.7411 |
|
name: gen_len |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZWEwMDFiYjgyNzRhZDVmOWIzYzZlZWU5OTFkYmU4YzI2Mjk2OTg1ZDVlNzU0YzNhOWI1MmU2NTAxZWUzZmFlOCIsInZlcnNpb24iOjF9.Zepow4AFj1sQ6zyJGoy_Dl4ICKRtzZI2nVYWlTsDnGrBDT42ak9mFUuw-BjHR8dEVHJKmOZlLk6GJ09bL7tGAA |
|
- task: |
|
type: summarization |
|
name: Summarization |
|
dataset: |
|
name: cnn_dailymail |
|
type: cnn_dailymail |
|
config: 3.0.0 |
|
split: test |
|
metrics: |
|
- type: rouge |
|
value: 30.5046 |
|
name: ROUGE-1 |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYmVmN2NkZDE1ZDAzZjhiYWJkNWJjZDIwNGNkY2EzOTVlNzA3OTZlYmEyNDE5NzQwNmI4NTdmM2M3YzlmMGJiYSIsInZlcnNpb24iOjF9.UbgnlgTUEd2yhULHeNKHQaVtAYwE3CijYGZc5mZSZkwXGIwJxwkDimhyo6XxMr8iCsu_hQLEsEtN9CWTn0SrDw |
|
- type: rouge |
|
value: 13.2577 |
|
name: ROUGE-2 |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYzU5MTg1OGJkYzAwNmUwMDdmYTBhODBhYjkyOTdjODRjMDJiNjU0ZjkzYzYyYWJhZTA1YjQ2NTliNWUyYmY3YiIsInZlcnNpb24iOjF9.fuvr3vrY8CSYpSluLeMz9VnxysWSlFFRMnnR3ZKZOxlh7_UNwtlMMHWCH6Yfy65LzglLNsRSnWNrwn5OXP4vAw |
|
- type: rouge |
|
value: 19.0306 |
|
name: ROUGE-L |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzMxYmY1ZmRlYzA4NzNkZDAzZjc0MzhkY2E4YTRiMmI0M2IwNDIwNzdiOWIzYTM1YTBmNWJkOWE3ODA3ZDM5NyIsInZlcnNpb24iOjF9.y-vzjHeER3iqyvSrjHUvy6Z_hom6aV0SRNV5CiB2efPmS7cL9nifoqpF2MJtip9RVn5nuuavlm-e3e2K0S5yDw |
|
- type: rouge |
|
value: 28.3421 |
|
name: ROUGE-LSUM |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNGJjYTdhMDIxYWVhYzM0MDRkYTM4MTI4YmRhOGEwYjE5OGU1NWNiYjIzOTdkM2FlNTUxNTJiNzBmNWQ1NDgyOSIsInZlcnNpb24iOjF9.32seuR1CHAtUR_UCCx1nTiv-u88ETqypzWt5iItexmFTlVkZjPw7whgM7KXtgJsPdWfdcClYif5Qpnbq-NycDA |
|
- type: loss |
|
value: 3.9484164714813232 |
|
name: loss |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYTgyMzJhNjY1OTU4YmVmMzVlYmI1N2IzZTdiNzYwMTA4YzRlZjY1ZjRhN2IxNTE5NjhkYjA1ZmMzMzVhNDk5NyIsInZlcnNpb24iOjF9.Cn8b20pksnlZF8LaJPxKrmrPMDIJ4CAPfjGifB86RaA6pLSTyY_wYsqEb2JfAczViquk4HtV8MvLnv0cioLODQ |
|
- type: gen_len |
|
value: 231.0762 |
|
name: gen_len |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYTIxOTMyZDlhNjYwOTY5M2Q0ZTZiNWQ5YzAxMjE1OTllOGNhNWU4MjQ0OTBhZTE5NDZjNmEzZTZlOWRiMGY5NyIsInZlcnNpb24iOjF9.QK29Zwhj8jN3hWae54-qaF7vHdh1ijSa6Mq_4LdGcG8xHLCerVGg45H9B1XClCksMadp7auOzPa8CEjxYVpyBA |
|
- task: |
|
type: summarization |
|
name: Summarization |
|
dataset: |
|
name: billsum |
|
type: billsum |
|
config: default |
|
split: test |
|
metrics: |
|
- type: rouge |
|
value: 36.8502 |
|
name: ROUGE-1 |
|
verified: true |
|
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|
value: 15.9147 |
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name: ROUGE-2 |
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verified: true |
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|
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|
value: 23.4762 |
|
name: ROUGE-L |
|
verified: true |
|
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|
- type: rouge |
|
value: 30.9597 |
|
name: ROUGE-LSUM |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzEzOGNiYjk4NDkxNTFmMjA5YjM1YTQzZTk2N2JiZDgxNzAxYzFlYjliZjA3NmRjMzZlNGYyODBkNTI1NzVjNiIsInZlcnNpb24iOjF9.C_hobTR0ZY958oUZcGEKj2RoPOkyfMCTznwi4mUx-bfGRRAecMyn45bWVwwRq12glk1vThDetCjOMHA6jgSDCw |
|
- type: loss |
|
value: 3.878790855407715 |
|
name: loss |
|
verified: true |
|
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|
- type: gen_len |
|
value: 131.3622 |
|
name: gen_len |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYmJjN2Q5ZGNlZjQ2ODJiYTZlMzZmNWVmMzRlMGQ0ZTkxZWM3ZDQ4ZmQ1NmUyZjY4MTVhZGE5NDFiZTBhNDZiYSIsInZlcnNpb24iOjF9.DqYNc0ZCX_EqRi4zbSBAtb-js_JBHSWZkeGR9gSwEkJletKYFxPGZWd-B1ez88aj6PO775-qHd98xx3IWCHECQ |
|
- task: |
|
type: summarization |
|
name: Summarization |
|
dataset: |
|
name: big_patent |
|
type: big_patent |
|
config: y |
|
split: test |
|
metrics: |
|
- type: rouge |
|
value: 33.7585 |
|
name: ROUGE-1 |
|
verified: true |
|
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|
- type: rouge |
|
value: 9.4101 |
|
name: ROUGE-2 |
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verified: true |
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|
- type: rouge |
|
value: 18.8927 |
|
name: ROUGE-L |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiODhhMGZiZWFlNmZkYmYxZjJmODE1NWRiZjI2OGU1MTc4MDkyYjk1Mzk5ODFkYWVhY2ExNTViYjJmYzkzNWJhYiIsInZlcnNpb24iOjF9.SkKhf-l2cl2KcuC17oPrBtkBlZJaj2ujCgzRlfZy76rU9JtlW7N9bcy1ugnw-vRVUVVR6wUK08T45YorfuxqBg |
|
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|
value: 28.5051 |
|
name: ROUGE-LSUM |
|
verified: true |
|
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|
- type: loss |
|
value: 5.162865161895752 |
|
name: loss |
|
verified: true |
|
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|
- type: gen_len |
|
value: 222.6626 |
|
name: gen_len |
|
verified: true |
|
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|
- task: |
|
type: summarization |
|
name: Summarization |
|
dataset: |
|
name: multi_news |
|
type: multi_news |
|
config: default |
|
split: test |
|
metrics: |
|
- type: rouge |
|
value: 38.7332 |
|
name: ROUGE-1 |
|
verified: true |
|
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verified: true |
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|
--- |
|
|
|
# Longformer Encoder-Decoder (LED) for Narrative-Esque Long Text Summarization |
|
|
|
|
|
<a href="https://colab.research.google.com/gist/pszemraj/36950064ca76161d9d258e5cdbfa6833/led-base-demo-token-batching.ipynb"> |
|
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> |
|
</a> |
|
|
|
- **What:** This is the (current) result of the quest for a summarization model that condenses technical/long information down well _in general, academic and narrative usage |
|
- **Use cases:** long narrative summarization (think stories - as the dataset intended), article/paper/textbook/other summarization, technical:simple summarization. |
|
- Models trained on this dataset tend to also _explain_ what they are summarizing, which IMO is awesome. |
|
- Works well on lots of text, and can hand 16384 tokens/batch. |
|
- See examples in Colab demo linked above, or try the [demo on Spaces](https://huggingface.co/spaces/pszemraj/summarize-long-text) |
|
|
|
- |
|
|
|
> Note: the API is set to generate a max of 64 tokens for runtime reasons, so the summaries may be truncated (depending on the length of input text). For best results use python as below. |
|
|
|
|
|
## About |
|
|
|
- Trained on the BookSum dataset released by SalesForce (this is what adds the `bsd-3-clause` license) |
|
- Trained for 16 epochs vs. [`pszemraj/led-base-16384-finetuned-booksum`](https://huggingface.co/pszemraj/led-base-16384-finetuned-booksum), |
|
|
|
- parameters adjusted for _very_ fine-tuning type training (super low LR, etc) |
|
- all the parameters for generation on the API are the same for easy comparison between versions. |
|
|
|
## Other Checkpoints on Booksum |
|
|
|
- See [led-large-book-summary](https://huggingface.co/pszemraj/led-large-book-summary) for LED-large trained on the same dataset. |
|
|
|
--- |
|
|
|
# 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 l compile the best _extractive_ summary from the input provided. |
|
- create the pipeline object: |
|
|
|
```python |
|
|
|
import torch |
|
from transformers import pipeline |
|
|
|
hf_name = 'pszemraj/led-base-book-summary' |
|
|
|
summarizer = pipeline( |
|
"summarization", |
|
hf_name, |
|
device=0 if torch.cuda.is_available() else -1, |
|
) |
|
``` |
|
|
|
- put words into the pipeline object: |
|
|
|
```python |
|
wall_of_text = "your words here" |
|
|
|
result = summarizer( |
|
wall_of_text, |
|
min_length=8, |
|
max_length=256, |
|
no_repeat_ngram_size=3, |
|
encoder_no_repeat_ngram_size=3, |
|
repetition_penalty=3.5, |
|
num_beams=4, |
|
do_sample=False, |
|
early_stopping=True, |
|
) |
|
print(result[0]['generated_text']) |
|
``` |
|
|
|
--- |
|
|
