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--- |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- summarization |
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- pegasus |
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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 |
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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 |
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- text: ' the big variety of data coming from diverse sources is one of the key properties |
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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 |
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be done with this data and how to design the best possible architecture to accomplish |
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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 |
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of services In parallel to the processing layer, the underlying data storage has |
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also changed and became more distributed This, in turn, required a significant |
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paradigm shift as the traditional approach to transactions (ACID) could no longer |
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be supported. On top of this, cloud computing is becoming a major approach with |
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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 |
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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 |
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companies have to relate their corporate resources to external services, e.g. |
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financial markets, weather forecasts, social media, etc While several of the sites |
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provide some sort of API to access the data in a more orderly fashion; countless |
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sources require advanced web mining and Natural Language Processing (NLP) processing |
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techniques: Advances in science push researchers to construct new instruments |
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for observing the universe O conducting experiments to understand even better |
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the laws of physics and other domains. Every year humans have at their disposal |
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new telescopes, space probes, particle accelerators, etc These instruments generate |
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huge streams of data, which need to be stored and analyzed. The constant drive |
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for efficiency in the industry motivates the introduction of new automation techniques |
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and process optimization: This could not be done without analyzing the precise |
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data that describe these processes. As more and more human tasks are automated, |
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machines provide rich data sets, which can be analyzed in real-time to drive efficiency |
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to new levels. Finally, it is now evident that the growth of the Internet of Things |
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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 |
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data sets have been made available through this medium, most of the resources |
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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, |
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etc, clearly show the potential lying in these resources. Those who can successfully |
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mine and interpret the Internet data can gain unique insight and competitive advantage |
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in their business An important area of data analytics on the edge of corporate |
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IT and the Internet is Web Analytics.' |
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example_title: data science textbook |
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inference: |
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parameters: |
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max_length: 64 |
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no_repeat_ngram_size: 2 |
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encoder_no_repeat_ngram_size: 3 |
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repetition_penalty: 2.4 |
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length_penalty: 0.5 |
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num_beams: 4 |
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early_stopping: true |
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base_model: google/pegasus-large |
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--- |
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# checkpoints |
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This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on the [booksum](https://github.com/salesforce/booksum) dataset. |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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- standard pegasus has a max input length of 1024 tokens, therefore the model only saw the first 1024 tokens of a chapter when training, and learned to try to make the chapter's summary from that. Keep this in mind when using this model, as information at the end of a text sequence longer than 1024 tokens may be excluded from the final summary/the model will be biased towards information presented first. |
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- this was only trained on the dataset for an epoch but still provides reasonable results. |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.001 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 256 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine_with_restarts |
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- lr_scheduler_warmup_ratio: 0.03 |
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- num_epochs: 1 |
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### Training results |
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### Framework versions |
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- Transformers 4.16.1 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 1.18.2 |
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- Tokenizers 0.10.3 |
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